# Map 1-based optional input ports to variables
dataset1 <- maml.mapInputPort(1) 
#dataset2 is dataset1 minus the last 3 lines of data
dataset2 <- head(dataset1, n=-3)
#dataset3 is the last 3 lines of data used for ci
dataset3 <- tail(dataset1, n=3)
#statistical model
model <- lm(energyuse ~ housesize1 + housesize2, data=dataset2)
f1 <- c(summary(model))
#title row
t1 <- cbind('estimates', 'std errors', 't values', 'prob..Ts')
#store the coefficients in the summary as a matrix
f2 <- round(as.matrix(f1$coefficients), 4)
#store the r stats
f3 <- cbind('R squared', round(f1$r.squared, 4), 'Adj R squared', round(f1$adj.r.squared, 4))
#store the fstats
f4 <- cbind('F statistic:', round(f1$fstatistic, 4), 'N/A', 'N/A')
ci <- predict(model, dataset3, interval='confidence')
#title row
t5 <- cbind('estimate', 'lower ci  ', 'upper ci', '')
f5 <- cbind(round(ci, 4), 'N/A')
#convert to dataframe for export (tratios, rstats, fstats, cis)
df1 <- data.frame(rbind(t1, f2, f3, f4, t5, f5))
# Select data.frame to be sent to the output Dataset port
maml.mapOutputPort('df1');