Metric Predicted Variable With One Nominal Predictor Variable


 Louise Austin
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1 Metric Predicted Variable With One Nominal Predictor Variable Tim Frasier Copyright Tim Frasier This work is licensed under the Creative Commons Attribution 4.0 International license. Click here for more information.
2 Goals & General Idea
3 Goals When would we use this type of analysis? When we want to know the effect of being in a group on some metric predictor variable Very common type of data set Monetary income (metric) and political party (nominal) Drug effect across groups etc. Frequently analyzed with a single factor (or oneway) ANOVA
4 General Idea Trying to quantify the relationship between two different sets of data One (y) is the metric response (or predicted) variable The other (x) is the nominal predictor variable that represents the categories in which measurements, samples, individuals can belong
5 Equation or Kruschke (2015) p. 555
6 Equation or Mean value of y, across all groupings Kruschke (2015) p. 555
7 Equation or Degree to which values are deflected above or below mean value, based on being in group j Kruschke (2015) p. 555
8 Equation or βj = 0, by definition Will add this constraint to our model Kruschke (2015) p. 555
9 The Data
10 Data Suppose you re studying a horse population that had a large dieoff in one year (from which you ve obtained samples) You re interested in the effect of inbreeding on survival in this population May expect the following patterns
11 Data Age Class Adults Juveniles Foals Prediction & Logic Lowest degree of inbreeding (have survived previous selection events; inbred individuals have already been weeded out ) Moderate degree of inbreeding (have survived some, but not too many, previous selection events; some inbred individuals have been weeded out ) Highest degree of inbreeding (have not had to survive any previous selection events; no inbred individuals have been weeded out yet, except in this event)
12 Data Metric predicted variable with one nominal predictor Inbreeding Coefficient Age Class 0.12 adult 0.23 juvenile 0.06 adult 0.22 foal 0.34 foal 0.17 juvenile
13 Getting A Feel for the Data Before we can create an appropriate model, we need to get a feel for the data I think two main plots would be useful here A box plot grouped by age class Points grouped by age class
14 Getting A Feel for the Data Put simhorsedata.csv file in R s working directory Load it into R horsedata < read.table( simhorsedata.csv, header = TRUE, sep =, )
15 Getting A Feel for the Data Make a box plot Make the aclass field a factor aclass < factor(horsedata$aclass, levels = c( adult, juvenile, foal ), ordered = TRUE)
16 Getting A Feel for the Data Make a box plot Make the aclass field a factor aclass < factor(horsedata$aclass, levels = c( adult, juvenile, foal ), ordered = TRUE) Note that we re specifying the order here, so that adult will be 1, juvenile will be 2, and foal will be 3. Otherwise, these would be ordered alphabetically (i.e., adult, foal, juvenile)
17 Getting A Feel for the Data Make the boxplot boxplot(horsedata$ic ~ aclass, ylab = Inbreeding Coefficient, xlab = Age Class, col = skyblue ) Inbreeding Coefficient adult juvenile foal Age Class
18 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal ))
19 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) Have to use numeric form, otherwise it defaults to a box plot.
20 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) Tell R not to plot the xaxis labels (we ll add our own later). These would be numbers.
21 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) Make points filled black circles that are opaque, so that you can see where there is overlap.
22 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) Function for adding a custom axis.
23 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) Position of the axis. 1 = bottom, 2 = left, 3 = top, 4 = right.
24 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 1, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) At what positions to add our custom labels (remember that as numeric, our categories are 1, 2, and 3)
25 Getting A Feel for the Data Can also plot the points to see raw data (requires a few tricks) plot(as.numeric(aclass), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 0, 0.25)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal )) What the labels should be at our indicated positions.
26 Getting A Feel for the Data Inbreeding Coefficient adult juvenile foal Age Class
27 Getting A Feel for the Data This is a little too clumped to be useful Can jitter the xvalues of the points to make it clearer This adds random noise to the data in the specified axis plot(jitter(as.numeric(aclass)), horsedata$ic, xaxt = n, ylab = Inbreeding Coefficient, xlab = Age Class, pch = 16, col = rgb(0, 0, 1, 0.5)) axis(1, at = c(1, 2, 3), labels = c( adult, juvenile, foal ))
28 Getting A Feel for the Data Inbreeding Coefficient adult juvenile foal Age Class
29 Frequentist Approach
30 Frequentist Approach Again, these type of data are typically analyzed with an ANOVA, which can be called with the aov function anovatest < aov(horsedata$ic ~ horsedata$aclass)
31 Frequentist Approach Can get the coefficient estimates by looking at the model tables print(model.tables(anovatest)) Tables of effects horsedata$aclass adult foal juvenile rep
32 Frequentist Approach Can get the coefficient estimates by looking at the model tables print(model.tables(anovatest)) Tables of effects horsedata$aclass adult foal juvenile rep Adults have lower inbreeding coefficients than the population average, those of juveniles are slightly lower than average, and those for foals are substantially above average.
33 Frequentist Approach Can get the coefficient estimates by looking at the model tables print(model.tables(anovatest)) Tables of effects horsedata$aclass adult foal juvenile rep Number of subjects in each category.
34 Frequentist Approach Can get the coefficient estimates by looking at the model tables print(model.tables(anovatest)) Tables of effects horsedata$aclass adult foal juvenile rep Note lack of confidence intervals in coefficient estimates.
35 Frequentist Approach Can see if this effect is significant summary(anovatest) Df Sum Sq Mean Sq F value Pr(>F) horsedata$aclass <2e16 *** Residuals Signif. codes: 0 *** ** 0.01 * Yep.
36 Bayesian Approach
37 Load Libraries & Functions library(runjags) library(coda) source( plotpost.r )
38 Prepare the Data Standardize metric (y) data y < horsedata$ic ymean < mean(y) ysd < sd(y) zy < (y  ymean) / ysd N < length(y)
39 Prepare the Data Organize the nominal (x) data x < as.numeric(horsedata$aclass) xnames < levels(as.factor(horsedata$aclass)) nageclass < length(unique(horsedata$aclass))
40 Prepare the Data Organize the nominal (x) data x < as.numeric(horsedata$aclass) xnames < levels(as.factor(horsedata$aclass)) nageclass < length(unique(horsedata$aclass)) Save the nominal data as x in numeric form (1, 2, and 3 instead of adult, foal, and juvenile )
41 Prepare the Data Organize the nominal (x) data x < as.numeric(horsedata$aclass) xnames < levels(as.factor(horsedata$aclass)) nageclass < length(unique(horsedata$aclass)) Make a vector ( xnames ) with the names of each nominal category. xnames is now adult, foal, juvenile.
42 Prepare the Data Organize the nominal (x) data x < as.numeric(horsedata$aclass) xnames < levels(as.factor(horsedata$aclass)) nageclass < length(unique(horsedata$aclass)) Get the number of categories in the data set (unique values in our nominal data set)
43 Define the Model
44 Define the Model µ τ = 1/σ 2  norm yi
45 Define the Model α gamma β µ τ = 1/σ 2  norm yi
46 Define the Model 0 10 µ τ = 1/σ 2  norm α gamma β µ τ = 1/σ 2  norm yi
47 Define the Model µ τ = 1/σ 2 µ τ = 1/σ 2  norm  norm α gamma β µ τ = 1/σ 2  norm yi
48 Define the Model µ τ = 1/σ 2 µ τ = 1/σ 2 norm norm  Same as before, data just coded differently!!! 1.1 (Will have some differences in actual model) α β gamma µ τ = 1/σ 2 norm yi
49 Define the Model modelstring = " model { # Likelihood # for (i in 1:N) { y[i] ~ dnorm(mu[i], tau) mu[i] < a0 + a1[x[i]] } # Priors # a0 ~ dnorm(0, 1/10^2) for (j in 1:nAgeClass) { a1[j] ~ dnorm(0, 1/10^2) } sigma ~ dgamma(1.1, 0.11) tau < 1 / sigma^2... (there s more)
50 Define the Model modelstring = " model { How to indicate that value of x[i] is categorical, rather than a number to be taken at face value # Likelihood # for (i in 1:N) { y[i] ~ dnorm(mu[i], tau) mu[i] < a0 + a1[x[i]] } # Priors # a0 ~ dnorm(0, 1/10^2) for (j in 1:nAgeClass) { a1[j] ~ dnorm(0, 1/10^2) } sigma ~ dgamma(1.1, 0.11) tau < 1 / sigma^2... (there s more)
51 Define the Model modelstring = " model { Using a instead of standard b (for beta) to indicate that these coefficients are not yet standardized to sum to zero # Likelihood # for (i in 1:N) { y[i] ~ dnorm(mu[i], tau) mu[i] < a0 + a1[x[i]] } # Priors # a0 ~ dnorm(0, 1/10^2) for (j in 1:nAgeClass) { a1[j] ~ dnorm(0, 1/10^2) } sigma ~ dgamma(1.1, 0.11) tau < 1 / sigma^2... (there s more)
52 Define the Model... # # # Convert a0 and a1[] to sumtozero b0 and b1[] # # # # Create matrix with values for each age class# for (j in 1:nAgeClass) { m[j] < a0 + a1[j] } # Make b0 the mean across all age classes # b0 < mean(m[1:nageclass] # Make b1[j] the difference between that category and b0 # for (j in 1:nAgeClass) { b1[j] < m[j]  b0 } } writelines(modelstring, con = model.txt )
53 Prepare Data for JAGS Specify as a list for JAGS datalist = list ( y = zy, x = x, N = N, nageclass = nageclass )
54 Specify Initial Values initslist < function() { list( sigma = rgamma(n = 1, shape = 1.1, rate = 0.11), a0 = rnorm(n = 1, mean = 0, sd = 10), a1 = rnorm(n = nageclass, mean = 0, sd = 10) ) }
55 Specify Initial Values initslist < function() { list( sigma = rgamma(n = 1, shape = 1.1, rate = 0.11), a0 = rnorm(n = 1, mean = 0, sd = 10), a1 = rnorm(n = nageclass, mean = 0, sd = 10) ) } Note that we need one for each category!
56 Specify MCMC Parameters and Run library(runjags) runjagsout < run.jags( method = simple, model = model.txt, monitor = c( b0, b1, sigma ), data = datalist, inits = initslist, n.chains = 3, adapt = 500, burnin = 1000, sample = 20000, thin = 1, summarise = TRUE, plots = FALSE)
57 Specify MCMC Parameters and Run library(runjags) runjagsout < run.jags( method = simple, model = model.txt, monitor = c( b0, b1, sigma ), data = datalist, inits = initslist, n.chains = 3, adapt = 500, burnin = 1000, sample = 20000, thin = 1, summarise = TRUE, plots = FALSE) Will keep track of all b1 values (one for each category)
58 Evaluate Performance of the Model
59 Testing Model Performance Retrieve the data and take a peak at the structure codasamples = as.mcmc.list(runjagsout) head(codasamples[[1]]) Markov Chain Monte Carlo (MCMC) output: Start = 1501 End = 1507 Thinning interval = 1 b0 b1[1] b1[2] b1[3] sigma
60 Testing Model Performance Trace plots par(mfrow = c(2,3)) traceplot(codasamples)
61 Testing Model Performance Autocorrelation plots autocorr.plot(codasamples[[1]]) b0 b1[1] Autocorrelation Autocorrelation Lag Lag b1[2] b1[3] Autocorrelation Autocorrelation Lag Lag sigma Autocorrelation Lag
62 Testing Model Performance Gelman & Rubin diagnostic gelman.diag(codasamples) Potential scale reduction factors: Point est. Upper C.I. b0 1 1 b1[1] 1 1 b1[2] 1 1 b1[3] 1 1 sigma 1 1 Multivariate psrf 1
63 Testing Model Performance Effective size effectivesize(codasamples) b0 b1[1] b1[2] b1[3] sigma
64 Viewing Results
65 Parsing Data Convert codasamples to a matrix Will concatenate chains into one long one mcmcchain = as.matrix(codasamples)
66 Parsing Data Separate out data for each parameter # sigma# zsigma = mcmcchain[, sigma ] # b0# zb0 = mcmcchain[, b0 ] # b1# chainlength = length(zb0) zb1 = matrix(0, ncol = chainlength, nrow = nageclass) for (j in 1:nAgeClass) { zb1[j, ] = mcmcchain[, paste("b1[", j, "]", sep = "")] }
67 Parsing Data Separate out data for each parameter Create a matrix to hold posteriors for each category in our Age Class variable: # sigma# zsigma = mcmcchain[, sigma ] One column for each step in the chain, One row per category # b0# zb0 = mcmcchain[, b0 ] # b1# chainlength = length(zb0) zb1 = matrix(0, ncol = chainlength, nrow = nageclass) for (j in 1:nAgeClass) { zb1[j, ] = mcmcchain[, paste("b1[", j, "]", sep = "")] }
68 Parsing Data Separate out data for each parameter # sigma# zsigma = mcmcchain[, sigma ] # b0# zb0 = mcmcchain[, b0 ] Fill each row (category) with the posteriors from the appropriatelynamed column from the mcmcchain # b1# chainlength = length(zb0) zb1 = matrix(0, ncol = chainlength, nrow = nageclass) for (j in 1:nAgeClass) { zb1[j, ] = mcmcchain[, paste("b1[", j, "]", sep = "")] }
69 Parsing Data Separate out data for each parameter # sigma# zsigma = mcmcchain[, sigma ] # b0# zb0 = mcmcchain[, b0 ] Will be: b1[1] b1[2] b1[3] # b1# chainlength = length(zb0) zb1 = matrix(0, ncol = chainlength, nrow = nageclass) for (j in 1:nAgeClass) { zb1[j, ] = mcmcchain[, paste("b1[", j, "]", sep = "")] }
70 Convert Back to Original Scale # sigma# sigma < zsigma * ysd # b0# b0 < zb0 * ysd + ymean # b1# b1 = matrix(0, ncol = chainlength, nrow = nageclass) for (j in 1:nAgeClass) { b1[j, ] = zb1[j, ] * ysd }
71 Plot Posterior Distributions Sigma par(mfrow = c(1, 1)) histinfo = plotpost(sigma, xlab = bquote(sigma)) mean = % HDI σ
72 Plot Posterior Distributions b0 histinfo = plotpost(b0, xlab = bquote(beta[0])) mean = % HDI β 0
73 Plot Posterior Distributions b1 par(mfrow = c(1, 3)) for (j in 1:nAgeClass) { histinfo = plotpost(b1[j, ], xlab = bquote(b1[.(j)]), main = paste( b1:, xnames[j])) }
74 Plot Posterior Distributions b1 Remember, these are effects of being in each category (deflections) To get actual values, add b0 to each b1: adult b1: foal b1: juvenile mean = mean = mean = % HDI % HDI % HDI b b b1 3
75 Comparing Groups
76 Comparing Groups eadults < b1[1, ] efoals < b1[2, ] ejuveniles < b1[3, ]
77 Comparing Groups eadults < b1[1, ] efoals < b1[2, ] ejuveniles < b1[3, ] e to indicate we are looking at effects rather than actual values
78 Comparing Groups Adult vs foal par(mfrow = c(1, 1)) AvF < eadults  efoals histinfo = plotpost(avf, main = "Adult v Foal", xlab = "") Adult v Foal mean = % HDI
79 Comparing Groups Adult vs juvenile AvJ < eadults  ejuveniles histinfo = plotpost(avj, main = "Adult v Juvenile", xlab = "") Adult v Juvenile mean = % HDI
80 Comparing Groups Foal vs juvenile FvJ < efoals  ejuveniles histinfo = plotpost(fvj, main = "Foal v Juvenile", xlab = "") Foal v Juvenile mean = % HDI
81 Comparing Groups Adult vs others JpF < (ejuveniles + efoals) / 2 AvO < eadults  JpF histinfo = plotpost(avo, main = "Adults v Others", xlab = "") Adults v Others mean = % HDI
82 Comparing Groups Foals vs others ApJ < (eadults + ejuveniles) / 2 FvO < efoals  ApJ histinfo = plotpost(fvo, main = "Foals v Others", xlab = "") Foals v Others mean = % HDI
83 Comparing Groups Dot charts Nice for comparing the same parameter across groups Need to make a new data frame containing the mean, and error bars for the parameter in each group
84 Comparing Groups Dot charts First, store the mean of each coefficient as a new vector b1means < c(mean(eadults), mean(efoals), mean(ejuveniles))
85 Comparing Groups Dot charts Get the highest density interval for each beta, and combine source( HDIofMCMC.R ) b1.adultshdi < HDIofMCMC(eAdults) b1.foalshdi < HDIofMCMC(eFoals) b1.juvenileshdi < HDIofMCMC(eJuveniles) b1hdi < rbind(b1.adultshdi, b1.foalshdi, b1.juvenileshdi)
86 Comparing Groups Dot charts Get the highest density interval for each beta, and combine source( HDIofMCMC.R ) b1.adultshdi < HDIofMCMC(eAdults) b1.foalshdi < HDIofMCMC(eFoals) b1.juvenileshdi < HDIofMCMC(eJuveniles) b1hdi < rbind(b1.adultshdi, b1.foalshdi, b1.juvenileshdi) Returns the upper and lower values
87 Comparing Groups Dot charts Get the highest density interval for each beta, and combine source( HDIofMCMC.R ) b1.adultshdi < HDIofMCMC(eAdults) b1.foalshdi < HDIofMCMC(eFoals) b1.juvenileshdi < HDIofMCMC(eJuveniles) b1hdi < rbind(b1.adultshdi, b1.foalshdi, b1.juvenileshdi) Can change what percentage to use with the credmass argument (i.e., to get the 89% HDI, credmass = 0.89)
88 Comparing Groups Dot charts Get the highest density interval for each beta, and combine source( HDIofMCMC.R ) b1.adultshdi < HDIofMCMC(eAdults) b1.foalshdi < HDIofMCMC(eFoals) b1.juvenileshdi < HDIofMCMC(eJuveniles) b1hdi < rbind(b1.adultshdi, b1.foalshdi, b1.juvenileshdi)
89 Comparing Groups Dot charts Plot the means of each group dotchart(b1means, pch = 16, labels = c("adult", "foal", juvenile"), xlim = c(min(b1hdi), max(b1hdi)), xlab = "Beta coefficient") juvenile foal adult Beta coefficients
90 Comparing Groups Dot charts Add the HDI bars segments(b1hdi[, 1], 1:3, b1hdi[, 2], 1:3, lwd = 2) juvenile foal adult Beta coefficients
91 Comparing Groups Dot charts Add the HDI bars segments(b1hdi[, 1], 1:3, b1hdi[, 2], 1:3, lwd = 2) juvenile x and foal ycoordinates of starting positions for line adult segments Beta coefficients
92 Comparing Groups Dot charts Add the HDI bars segments(b1hdi[, 1], 1:3, b1hdi[, 2], 1:3, lwd = 2) juvenile x and foal ycoordinates of ending positions for line adult segments Beta coefficients
93 Comparing Groups Dot charts Add the HDI bars segments(b1hdi[, 1], 1:3, b1hdi[, 2], 1:3, lwd = 2) juvenile foal Make line twice as thick as default adult Beta coefficients
94 Comparing Groups Dot charts Add the HDI bars segments(b1hdi[, 1], 1:3, b1hdi[, 2], 1:3, lwd = 2) juvenile foal adult Beta coefficients
95 Check Validity of Model: Posterior Predictive Check
96 Posterior Predictive Check Generate new y values for a subset of x values in the data set based on estimates for coefficients Compare these predicted y values to the real ones
97 Posterior Predictive Check Select a subset of the data on which to make predictions (let s pick 50) newrows < seq(from = 1, to = NROW(horsedata), length = 50)
98 Posterior Predictive Check Select a subset of the data on which to make predictions (let s pick 30) npred < 30 newrows < seq(from = 1, to = NROW(horsedata), length = npred) newrows [1] [10] [19] [28] [37] [46]
99 Posterior Predictive Check newrows < round(newrows) newrows [1] [27]
100 Posterior Predictive Check Parse out these data from the original data frame newdata < horsedata[newrows, ]
101 Posterior Predictive Check Order based on categorical (predictor) variable to make plots clearer later newdata < newdata[order(newdata$aclass), ]
102 Posterior Predictive Check Separate out just the x data too, on which we will make predictions xnew < newdata$aclass xnewnums < as.numeric(xnew)
103 Posterior Predictive Check Organize categorical coefficients into one data frame (makes indexing later easier) b < rbind(eadults, efoals, ejuveniles)
104 Posterior Predictive Check Next, define a matrix that will hold all of the predicted y values Number of rows is the number of x values for prediction Number of columns is the number of y values generated from the MCMC process We ll start with the matrix filled with zeros, but will fill it in later postsampsize = length(b1) ynew = matrix(0, nrow = length(xnew), ncol = postsampsize)
105 Posterior Predictive Check Define a matrix for holding the HDI limits of the predicted y values Same number of rows as above Only two columns (one for each end of the HDI) yhdilim = matrix(0, nrow = length(xnew), ncol = 2)
106 Posterior Predictive Check Now, populate the ynew matrix by generating one predicted y value for each step in the chain for (i in 1:postSampSize) { ynew[, i] < rnorm(length(xnew), mean = b0 + b[xnewnums], sd = sigma) }
107 Posterior Predictive Check Now, populate the ynew matrix by generating one predicted y value for each step in the chain for (i in 1:postSampSize) { ynew[, i] < rnorm(length(xnew), mean = b0 + b[xnewnums], sd = sigma) } For every step in the chain, fill out a new column (all rows) of the new matrix...
108 Posterior Predictive Check Now, populate the ynew matrix by generating one predicted y value for each step in the chain for (i in 1:postSampSize) { ynew[, i] < rnorm(length(xnew), mean = b0 + b[xnewnums], sd = sigma) }...pulling the same number of x values as in our xnew list from a normal distribution...
109 Posterior Predictive Check Now, populate the ynew matrix by generating one predicted y value for each step in the chain for (i in 1:postSampSize) { ynew[, i] < rnorm(length(xnew), mean = b0 + b[xnewnums], sd = sigma) }...with a mean based on b0 plus which category each x value is in...
110 Posterior Predictive Check Now, populate the ynew matrix by generating one predicted y value for each step in the chain for (i in 1:postSampSize) { ynew[, i] < rnorm(length(xnew), mean = b0 + b[xnewnums], sd = sigma) }...and a standard deviation based on those data from the posterior.
111 Posterior Predictive Check Calculate means for each prediction, and the associated low and high 95% HDI estimates means < rowmeans(ynew) source("hdiofmcmc.r") for (i in 1:length(xNew)) { yhdilim[i, ] < HDIofMCMC(yNew[i, ]) }
112 Posterior Predictive Check Combine the data predtable < cbind(xnew, means, yhdilim)
113 Posterior Predictive Check Plot the results # Plot the predicted values (dot plot) # dotchart(means, labels = 1:nPred, xlim = c(min(yhdilim), max(yhdilim)), xlab = "Inbreeding Coefficient", pch = 16) segments(yhdilim[, 1], 1:nPred, yhdilim[, 2], 1:nPred, lwd = 2) # Plot the true values # points(x = newdata$ic, y = 1:nPred, pch = 16, col = rgb(1, 0, 0, 0.5))
114 Posterior Predictive Check Inbreeding Coefficient
115 Questions?
116 Homework!
117 Homework Current model assumes equal standard deviation (precision) for each category Modify the model to estimate a different standard deviation for each category, and evaluate in the same ways as we did the mean
118 Creative Commons License Anyone is allowed to distribute, remix, tweak, and build upon this work, even commercially, as long as they credit me for the original creation. See the Creative Commons website for more information. Click here to go back to beginning
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