3_GeneralizedLinearMEM

# Fit a glm using data in a long format
fitLong <- glm(mortality ~ dose, data = dfLong, family = "binomial")
summary(fitLong)

# Fit a glm using data in a short format with two columns
fitShort <- glm(cbind(mortality, survival) ~ dose, data = dfShort, family = "binomial")
summary(fitShort)

# Fit a glm using data in a short format with weights
fitShortP <- glm(mortalityP ~ dose, data = dfShort, weights = nReps, family = "binomial")
summary(fitShortP)
#
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# Fit the linear model
summary(lm(y ~ x))

# Fit the generalized linear model
summary(glm(y ~ x, family = "poisson"))
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# Plot the data using jittered points and the default stat_smooth
ggplot(data = dfLong, aes(x = dose, y = mortality)) + 
	geom_jitter(height = 0.05, width = 0.1) +
	stat_smooth(fill = 'pink', color = 'red') 
				
# Plot the data using jittered points and the the glm stat_smooth
ggplot(data = dfLong, aes(x = dose, y = mortality)) + 
	geom_jitter(height = 0.05, width = 0.1) +
	stat_smooth(method = 'glm',  
				method.args = list(family = "binomial"))
#
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# Load lmerTest
library(lmerTest)

# Fit glmerOut and look at its summary
glmerOut <- glmer(mortality ~ dose + (1 | replicate), family = 'binomial', data = df)
summary(glmerOut)
#
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# Load lmerTest
library(lmerTest)

# Fit the model and look at its summary 
modelOut <- glmer(cbind(Purchases, Pass) ~ friend + ranking + (1 | city), data = allData, family = 'binomial')
summary(modelOut) 

# Compare outputs to a lmer model
summary(lmer(Purchases/(Purchases + Pass) ~ friend + ranking + (1 | city), data = allData))
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# Run the code to see how to calculate odds ratios
summary(modelOut) 
exp(fixef(modelOut)[2])
exp(confint(modelOut)[3,])
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# Load lmerTest
library(lmerTest)

# Fit a Poisson glmer
summary(glmer(clicks ~ webpage + (1 | group), family = 'poisson', data = userGroups))
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# Load lmerTest
library(lmerTest)

# Age goes before year
# Age and year as fixed effects, then year as a random effect of county
modelOut <- glmer(count ~ age + year + (year|county), family = 'poisson',
                  data = ILdata)
summary(modelOut)
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# Extract out fixed effects
fixef(modelOut)
# Extract out random effects 
ranef(modelOut)

# Run code to see one method for plotting the data
ggplot(data = ILdata2, aes(x = year, y = count, group = county)) +
    geom_line() +
    facet_grid(age ~ .) +
    stat_smooth(method = 'glm',
                method.args = list(family = "poisson"), se = FALSE,
                alpha = 0.5) +
    theme_minimal()
#
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    # Extract out fixed effects
fixef(modelOut)
# Extract out random effects 
ranef(modelOut)

# Run code to see one method for plotting the data
ggplot(data = ILdata2, aes(x = year, y = count, group = county)) +
    geom_line() +
    facet_grid(age ~ . ) +
    stat_smooth(method = 'glm',
                method.args = list(family = "poisson"), se = FALSE,
                alpha = 0.5) +
    theme_minimal()