Statistical Modellig
By: varsha10 • March 7, 2019 • Coursework • 2,569 Words (11 Pages) • 729 Views
Stat Modelling Assignment-3
Varsha
February 28, 2019
Packages Required
library(tidyverse) #to visualize, transform, input, tidy and join data
library(dplyr) #data wrangling
library(kableExtra) #to create HTML Table
library(DT) #to preview the data sets
library(xlsx) #to load excel files
library(nnet) #to implement multinomial function
Taking Distance as a multinomial factor
Loaded the datasets and did initial data cleaning(detailed steps were performed in the first assignment)
faa1 <- read.xlsx("FAA1.xls", sheetName = "FAA1")
faa2 <- read.xlsx("FAA2_2.xls", sheetName = "Sheet1")
faa <- bind_rows(faa1, faa2)
check <- faa %>%
select(-duration) %>%
duplicated() %>%
which()
faa <- faa[-check,]
faa_check <- faa %>%
filter((duration > 40| is.na(duration)) & (speed_ground >= 30) & (speed_ground <= 140) &
(height >= 6) & (distance < 6000))
faa <- faa_check
We will create a mulitnomial variabe for distance.
faa1 <- faa %>%
mutate(Y = (ifelse(distance < 1000, 1,
ifelse( distance >= 1000 & distance < 2000, 2, 3)) ))
faa1$distance <- NULL
Now, we will use multinomial model to fit Y. We treat the new variable Y as categorical under the assumption that the levels of Y have no natural ordering.
faa1$Y <- as.factor(faa1$Y)
faa1 <- select(faa1, -speed_air ) %>%
na.omit()
mmod <- multinom(Y ~ aircraft + duration +
no_pasg + speed_ground + pitch + height , faa1)
## # weights: 24 (14 variable)
## initial value 858.016197
## iter 10 value 581.199724
## iter 20 value 236.239538
## iter 30 value 226.272595
## iter 40 value 226.221087
## iter 50 value 226.174784
## final value 226.164124
## converged
#summary(mmod)
Based on AIC, we get the model as -
mmodi <- step(mmod)
## Start: AIC=480.33
## Y ~ aircraft + duration + no_pasg + speed_ground + pitch + height
##
## trying - aircraft
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 599.571323
## iter 20 value 336.226736
## iter 30 value 334.049994
## iter 40 value 334.045290
## final value 334.043139
## converged
## trying - duration
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 518.225762
## iter 20 value 239.417331
## iter 30 value 228.258109
## iter 40 value 227.608269
## final value 227.113768
## converged
## trying - no_pasg
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 602.083949
## iter 20 value 240.843149
## iter 30 value 229.631112
## iter 40 value 228.963807
## final value 228.210500
## converged
## trying - speed_ground
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 804.987828
## final value 794.030499
## converged
## trying - pitch
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 582.954258
## iter 20 value 237.697711
## iter 30 value 228.361531
## iter 40 value 227.746783
## final value 227.327445
## converged
## trying - height
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 535.918721
## iter 20 value 302.074403
## iter 30 value 298.921547
## iter 40 value 298.913287
## final value 298.909431
## converged
## Df AIC
## - duration 12 478.2275
## - pitch 12 478.6549
##
## - no_pasg 12 480.4210
## - height 12 621.8189
## - aircraft 12 692.0863
## - speed_ground 12 1612.0610
## # weights: 21 (12 variable)
## initial value 858.016197
## iter 10 value 518.225762
## iter 20 value 239.417331
## iter 30 value 228.258109
## iter 40 value 227.608269
## final value 227.113768
## converged
##
## Step: AIC=478.23
## Y ~ aircraft + no_pasg + speed_ground + pitch + height
##
## trying - aircraft
## # weights: 18 (10 variable)
## initial value 858.016197
## iter 10 value 465.319936
## iter 20 value 335.496129
## iter 30 value 335.298663
## final value 335.293692
## converged
## trying - no_pasg
## # weights: 18 (10 variable)
## initial value 858.016197
## iter 10 value 444.634863
## iter 20 value 238.604117
## iter 30 value 230.208561
## iter 40 value 229.083783
## iter 40 value 229.083781
## iter 40 value 229.083781
## final value 229.083781
## converged
## trying - speed_ground
## # weights: 18 (10 variable)
## initial value 858.016197
## iter 10 value 799.384355
## final value 796.687299
## converged
## trying - pitch
## # weights: 18 (10 variable)
## initial value 858.016197
## iter 10 value 488.119245
## iter 20 value 236.788749
## iter 30 value 228.715466
## iter 40 value 228.221740
## final value 228.220496
## converged
## trying - height
## # weights: 18 (10 variable)
## initial value 858.016197
## iter 10 value 454.961171
## iter 20 value 300.709209
## iter 30 value 300.281087
## final value 300.277702
## converged
## Df AIC
## - pitch 10 476.4410
## - no_pasg 10 478.1676
##
## - height 10 620.5554
## - aircraft 10 690.5874
## - speed_ground 10 1613.3746
## # weights: 18 (10 variable)
## initial value 858.016197
## iter 10 value 488.119245
## iter 20 value 236.788749
## iter 30 value 228.715466
## iter 40 value 228.221740
## final value 228.220496
## converged
##
## Step: AIC=476.44
## Y ~ aircraft + no_pasg + speed_ground + height
##
## trying - aircraft
## # weights: 15 (8 variable)
## initial value 858.016197
## iter 10 value 394.729448
## iter 20 value 347.225190
## iter 30 value 347.118971
## final value 347.118955
## converged
## trying - no_pasg
## # weights: 15 (8 variable)
## initial value 858.016197
## iter 10 value 354.469061
## iter 20 value 242.186395
## iter 30 value 231.398332
## final value 230.124753
## converged
## trying - speed_ground
## # weights: 15 (8 variable)
## initial value 858.016197
## iter 10 value 797.491849
## final value 797.489048
## converged
## trying - height
## # weights: 15 (8 variable)
## initial value 858.016197
## iter 10 value 363.948352
## iter 20 value 301.923434
## iter 30 value 301.017662
## final value 301.004927
## converged
## Df AIC
## - no_pasg 8 476.2495
##
## - height 8 618.0099
## - aircraft 8 710.2379
## - speed_ground 8 1610.9781
## # weights: 15 (8 variable)
## initial value 858.016197
## iter 10 value 354.469061
## iter 20 value 242.186395
## iter 30 value 231.398332
## final value 230.124753
## converged
##
## Step: AIC=476.25
## Y ~ aircraft + speed_ground + height
##
## trying - aircraft
## # weights: 12 (6 variable)
## initial value 858.016197
## iter 10 value 378.591341
## iter 20 value 351.025155
## final value 350.780781
## converged
## trying - speed_ground
## # weights: 12 (6 variable)
## initial value 858.016197
## iter 10 value 797.741960
## final value 797.741923
## converged
## trying - height
## # weights: 12 (6 variable)
## initial value 858.016197
## iter 10 value 345.462031
## iter 20 value 304.390327
## final value 301.955008
## converged
## Df AIC
##
## - height 6 615.9100
## - aircraft 6 713.5616
## - speed_ground 6 1607.4838
...