MARS in R

 #MARS

library(earth)

require(MASS)

# fit model

fit <- earth(formula=SPEEDING_CRASH ~

               log(AVG_AADT)+LANDUSE_MIX+PAVEMENT_CONDITION+ISLDWIDTH+

               P_BOTH_SIDEWALK_ONLY+HIGH_FREQ_TRANSIT+SIGNAL_PER_MILE+

               MEDIAN_WIDTH+DAILY_TRANSIT+PAVEMENT_CONDITION+LANE_WIDTH

             ,  a, degree=1)

fit

plot(fit)

# summarize the fit

summary(fit)

# summarize the importance of input variables

evimp(fit)

plot(evimp(fit))

# make predictions

predictions <- predict(fit, a)

plot(fit)

# summarize accuracy

coefficients(fit)

residuals(fit)

mse <- mean((a$SPEEDING_CRASH - predictions)^2)

print(mse)

#standart error

vcov(fit)

standard_error(fit)

Std.Error(fit)

se <- sqrt(diag(vcov(fit)))

se.coef(fit)

sqrt(diag(cov(fit)))

#2 tailed z test

z<- coef(fit)/standard_error(fit)

p<- (1-pnorm(abs(z),0,1))*2

p        #pvalue