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homework2.R
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homework2.R
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climateData <- read.csv("~/Downloads/climateData.csv", stringsAsFactors = TRUE)
# Load packages
library(psych)
library(rties)
library(brms)
library(bayesplot)
library(bayestestR)
library(car)
options(scipen = 999)
#organizing my data
summary(climateData)
histAll(climateData)
datasub <- subset(climateData, select = c(proBx, econStrain, pce))
describe(datasub, IQR = T, quant = c(.25, .75))
histAll(datasub)
datastats<-get_summary_stats(datasub)
flextable(datastats)
# Visualize potential associations (checking for linearity)
plot(proBx ~ econStrain, data = datasub)
abline(glm(proBx ~ econStrain, data = datasub, family = "gaussian"))
plot(proBx ~ pce, data = datasub)
abline(glm(proBx ~ pce, data = datasub, family = "gaussian"))
pairs.panels(datasub, lm = T)
# Centering the variables
datasub$proBx_centered <- datasub$proBx - mean(datasub$proBx)
datasub$econStrain_centered <- datasub$econStrain - mean(datasub$econStrain)
datasub$pce_centered <- datasub$pce - mean(datasub$pce)
#models NHST
m1f <- glm(proBx_centered ~ econStrain_centered, data = datasub,
family = "gaussian", na.action = na.omit)
m2f <- glm(proBx_centered ~ pce_centered, data = datasub,
family = "gaussian", na.action = na.omit)
#1. Normality of Residuals
hist(resid(m1f), breaks = 10)
summary(resid(m1f))
qqPlot(resid(m1f))
hist(resid(m2f), breaks = 10)
summary(resid(m2f))
qqPlot(resid(m2f))
#2. Homoscedasticity (Constant Variance):
plot(resid(m1f) ~ econStrain_centered, data = datasub)
abline(h = 0)
plot(resid(m2f) ~ econStrain_centered, data = datasub)
abline(h = 0)
summary(m1f)
confint.lm(m1f)
r_sq <- function(model_name) {
r2 <- 1 - model_name$deviance/model_name$null.deviance
return(r2)
}
r_sq(m1f)
summary(m2f)
confint.lm(m2f)
r_sq(m2f)
#Bayesian
m1b <- brm(proBx_centered ~ 0 + Intercept + econStrain_centered, family = "gaussian",
chains = 4, iter = 2000, seed = 123, data = datasub)
m2b <- brm(proBx_centered ~ 0 + Intercept + pce_centered, family = "gaussian",
chains = 4, iter = 2000, seed = 123, data = datasub)
prior_summary(m1b)
### Now let's check for evidence of convergence:
plot(m1b) # looking for fuzzy caterpillars in the trace plots
summary(m1b) # with special focus on the ESS and Rhat values
### Check Model Assumptions (including accuracy of specified likelihood distribution)
pp_check(m1b, ndraws = 100) # how accurate do the draws based on the normal distribution appear?
pp_check(m1b, type = "error_hist", ndraws = 12, set.seed(456)) # check normality of residuals around 0
pp_check(m1b, type = "error_scatter_avg_vs_x", x = "relstressA_c", ndraws = 20, set.seed(678)) #check for constant variance
summary(m1b)
plot(m1b)
###### Model Effect Size (R-squared)
bayes_R2(m1b)
##### Probability of Direction
pd <- p_direction(m1b)
pd
plot(pd)
##### Region of Practical Equivalence
r <- rope(m1b)
r
plot(r)
#Second Model
prior_summary(m2b)
plot(m2b)
summary(m2b)
### Check Model Assumptions
pp_check(m2b, ndraws = 100)
pp_check(m2b, type = "error_hist", ndraws = 12, set.seed(456))
pp_check(m2b, type = "error_scatter_avg_vs_x", x = "relstressA_c", ndraws = 20, set.seed(678))
summary(m2b)
plot(m2b)
###### Model Effect Size (R-squared)
bayes_R2(m2b)
##### Probability of Direction
pd <- p_direction(m2b)
pd
plot(pd)
##### Region of Practical Equivalence
r <- rope(m2b)
r
plot(r)