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fitCubicAndStorage<-function(data_in, ldata_out, start_ind, temp3, R){
#################################
# Fit a cubic model #
#################################
# Initialise the quadratic of the time data
data_in2 = data_in^2
# Initialise the cubic of the time data
data_in3 = data_in^3
# Fit cubic model
glm.cubic<-glm(ldata_out ~ data_in + data_in2 + data_in3)
# Find the AIC value associated with the cubic model
AIC3 = AIC(glm.cubic)
mean1 = mean(ldata_out)
# Storage for the cubic model
R[start_ind,6] = glm.cubic$coefficients[[2]] # Slope coefficient (associated with data_in)
R[start_ind,8] = glm.cubic$coefficients[[3]] # Slope coefficient (associated with data_in^2)
R[start_ind,10] = glm.cubic$coefficients[[4]] # Slope coefficient (associated with data_in^3)
# Isolate the standard errors for the cubic model
SEs = sqrt(diag( vcov(glm.cubic)))
R[start_ind,7] = SEs[[2]] # SE of slope (associated with data_in)
R[start_ind,9] = SEs[[3]] # SE of slope (associated with data_in^2)
R[start_ind,11] = SEs[[4]] # SE of slope (associated with data_in^3)
R[start_ind,12] = 1 - sum(residuals(glm.cubic)^2)/sum((ldata_out-mean1)^2) # Store R2 for cubic model
R[temp3, 13] <-fitted.values(glm.cubic) # Store fitted values
R[temp3, 14] <- residuals(glm.cubic) # Store residual values
if (length(data_in)>5){
R[temp3, 26] <-ls.diag(glm.cubic)$stud.res # Store student residual values
R[temp3,27] <-ls.diag(glm.cubic)$std.res # Store standardised residual values
}
R[start_ind, 17] <-summary.glm(glm.cubic)$coefficients[[2,4]] # Store p-value associated with slope in cubic model (associated with data_in)
R[start_ind, 18] <-summary.glm(glm.cubic)$coefficients[[3,4]] # Store p-value associated with slope in cubic model (associated with data_in^2)
R[start_ind, 19] <-summary.glm(glm.cubic)$coefficients[[4,4]] # Store p-value associated with slope in cubic model (associated with data_in^3)
R[start_ind,20] = AIC3 # Store AIC for cubic model
return(list(glm.cubic = glm.cubic, R=R, AIC3 = AIC3))
}
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