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# R Script: Chi-Square Tests for Parametric and Non-parametric Models

# Chi square tests for parametric and non-parametric models

# Conduct chi-square tests on nested parametric models
for (i in 1:length(taxa.names)) {

print(taxa.names[i])
resp <- dfmerge[,taxa.names[i]] > 0

# Fit a model that is only a constant
modcmp <- glm(resp ~ 1, family = binomial, data = dfmerge)

# Compare original model with constant model using
# a chi-square statistic
modout <- anova(modlist.glm, modcmp, test = "Chi")
print(modout)

# Select p < 0.05 as statistically significant
if (modout[2,"P(>|Chi|)"] < 0.05) {
print("Model significant compared to constant")
}

# Fit a model with only a linear explanatory variable
modcmp <- glm(resp ~ temp, family = binomial, data = dfmerge)

# Compare original model with constant model using a
# chi-square statistic
modout <- anova(modlist.glm, modcmp, test = "Chi")
print(modout)
if (modout[2,"P(>|Chi|)"] < 0.05) {
print("Model with b2 significant (p < 0.05) improvement over linear model")
}

}

# Conduct chi-square tests on nested non-parametric models
library(gam)
for (i in 1:length(taxa.names)) {

print(taxa.names[i])
resp <- dfmerge[,taxa.names[i]] > 0
modcmp <- gam(resp ~ 1, family = binomial, data = dfmerge)
modout <- anova(modlist.gam, modcmp, test = "Chi")
print(modout)
if (modout[2,"P(>|Chi|)"] < 0.05) {
print("Model significant compared to constant")
}

# Fit a model with only a linear explanatory variable
modcmp <- gam(resp ~ temp, family = binomial, data = dfmerge)
modout <- anova(modlist.glm, modcmp, test = "Chi")
print(modout)
if (modout[2,"P(>|Chi|)"] < 0.05) {
print("Nonparametric model with two degrees of freedom significant over linear model.")
}

}

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