Fitting Multinomial Logistic Regression model in "Divide and Recombine" approach to Large Data Sets
drglm.multinom.RdFunction drglm.multinom fits multinomial logistic regressiosn model to big data sets in divide and recombine approach.
Arguments
- formula
An entity belonging to the "formula" class (or one that can be transformed into that class) represents a symbolic representation of the model that needs to be adjusted. Specifics about how the model is defined can be found in the 'Details' section.
- data
A data frame, list, or environment that is not required but can be provided if available.
- k
Number of subsets to be used.
Value
A "Multinomial (Polytomous) Logistic Regression Model" is fitted in "Divide and Recombine" approach.
References
Karim, M. R., & Islam, M. A. (2019). Reliability and Survival Analysis. In Reliability and Survival Analysis. Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, https://www.stats.ox.ac.uk/pub/MASS4/.
Examples
set.seed(123)
#Number of rows to be generated
n <- 10000
#creating dataset
dataset <- data.frame( pred_1 = round(rnorm(n, mean = 50, sd = 10)),
pred_2 = round(rnorm(n, mean = 7.5, sd = 2.1)),
pred_3 = as.factor(sample(c("0", "1"), n, replace = TRUE)),
pred_4 = as.factor(sample(c("0", "1", "2"), n, replace = TRUE)),
pred_5 = as.factor(sample(0:15, n, replace = TRUE)),
pred_6 = round(rnorm(n, mean = 60, sd = 5)))
#fitting multinomial logistic regression model
mmodel=drglm::drglm.multinom(
pred_4~ pred_1+ pred_2+ pred_3+ pred_5+ pred_6, data=dataset, k=10)
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1081.842250
#> iter 20 value 1079.724677
#> iter 30 value 1079.709082
#> final value 1079.708962
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1084.321143
#> iter 20 value 1075.145510
#> iter 30 value 1075.060898
#> final value 1075.059122
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1086.065933
#> iter 20 value 1080.654708
#> iter 30 value 1080.617651
#> final value 1080.617432
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1085.721140
#> iter 20 value 1082.392343
#> iter 30 value 1082.378604
#> final value 1082.378515
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1083.460043
#> iter 20 value 1076.975921
#> iter 30 value 1076.930816
#> final value 1076.930268
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1086.343504
#> iter 20 value 1083.483907
#> iter 30 value 1083.429147
#> final value 1083.428788
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1086.383304
#> iter 20 value 1079.248191
#> iter 30 value 1079.131777
#> final value 1079.129705
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1087.658835
#> iter 20 value 1079.056688
#> iter 30 value 1078.843593
#> final value 1078.841904
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1080.330228
#> iter 20 value 1073.171327
#> iter 30 value 1072.982368
#> final value 1072.981502
#> converged
#> # weights: 63 (40 variable)
#> initial value 1098.612289
#> iter 10 value 1082.581872
#> iter 20 value 1079.435676
#> iter 30 value 1079.288356
#> final value 1079.287908
#> converged
#Output
mmodel
#> Estimate.1 Estimate.2 standard error.1 standard error.2
#> (Intercept) -0.2853539188 -0.533750309 0.352355572 0.351293926
#> pred_1 0.0038416692 0.002793053 0.002519101 0.002511803
#> pred_2 0.0082891106 -0.009496117 0.011828815 0.011781775
#> pred_31 0.0218560016 0.098556010 0.050219507 0.050048272
#> pred_51 0.1041197912 0.107943130 0.143050124 0.140461598
#> pred_52 0.0185882466 0.034426012 0.141289751 0.138335472
#> pred_53 0.2397209895 0.080935362 0.141463492 0.141642918
#> pred_54 0.0938156731 -0.031423995 0.141581660 0.141524963
#> pred_55 0.1328703482 0.033111908 0.141310183 0.140318969
#> pred_56 0.2595421380 0.084977571 0.140523643 0.140900389
#> pred_57 -0.0258373204 0.006160637 0.142934906 0.139626085
#> pred_58 0.1254919206 0.064560739 0.143771676 0.141982106
#> pred_59 0.1020629948 0.024347754 0.141325495 0.139770477
#> pred_510 0.1031342956 -0.001880707 0.142115285 0.142460092
#> pred_511 0.1036721356 0.098304574 0.141672817 0.138927665
#> pred_512 0.1629845156 -0.009769957 0.141233081 0.142472377
#> pred_513 0.0560079157 0.033235440 0.140878949 0.138964031
#> pred_514 -0.0114576021 0.056310921 0.144333825 0.140790079
#> pred_515 -0.0323858251 -0.020269661 0.143521586 0.140359894
#> pred_6 -0.0004666329 0.007235091 0.004998638 0.004979892
#> z value.1 z value.2 Pr(>|z|).1 Pr(>|z|).2 95% lower CI.1
#> (Intercept) -0.80984648 -1.51938383 0.41802842 0.12866591 -0.975958149
#> pred_1 1.52501623 1.11197121 0.12725505 0.26615053 -0.001095677
#> pred_2 0.70075579 -0.80600053 0.48345543 0.42024255 -0.014894941
#> pred_31 0.43520940 1.96921904 0.66341044 0.04892794 -0.076572423
#> pred_51 0.72785530 0.76848855 0.46670217 0.44219699 -0.176253300
#> pred_52 0.13156118 0.24885889 0.89533139 0.80346994 -0.258334577
#> pred_53 1.69457848 0.57140423 0.09015541 0.56772567 -0.037542360
#> pred_54 0.66262589 -0.22203853 0.50757019 0.82428389 -0.183679281
#> pred_55 0.94027440 0.23597599 0.34707683 0.81345130 -0.144092521
#> pred_56 1.84696420 0.60310388 0.06475233 0.54643959 -0.015879141
#> pred_57 -0.18076285 0.04412239 0.85655373 0.96480684 -0.305984588
#> pred_58 0.87285566 0.45471039 0.38274176 0.64931761 -0.156295386
#> pred_59 0.72218388 0.17419812 0.47018143 0.86170976 -0.174929886
#> pred_510 0.72570868 -0.01320164 0.46801738 0.98946692 -0.175406544
#> pred_511 0.73177154 0.70759538 0.46430802 0.47919656 -0.174001483
#> pred_512 1.15401090 -0.06857440 0.24849570 0.94532840 -0.113827237
#> pred_513 0.39756057 0.23916578 0.69095413 0.81097704 -0.220109750
#> pred_514 -0.07938265 0.39996370 0.93672827 0.68918326 -0.294346701
#> pred_515 -0.22565125 -0.14441206 0.82147268 0.88517510 -0.313682965
#> pred_6 -0.09335200 1.45286115 0.92562392 0.14626231 -0.010263784
#> 95% lower CI.2 95% upper CI.1 95% upper CI.2
#> (Intercept) -1.2222737520 0.405250311 0.154773134
#> pred_1 -0.0021299910 0.008779015 0.007716097
#> pred_2 -0.0325879707 0.031473162 0.013595738
#> pred_31 0.0004631993 0.120284427 0.196648820
#> pred_51 -0.1673565430 0.384492883 0.383242804
#> pred_52 -0.2367065310 0.295511070 0.305558555
#> pred_53 -0.1966796557 0.516984339 0.358550379
#> pred_54 -0.3088078254 0.371310627 0.245959836
#> pred_55 -0.2419082168 0.409833218 0.308132032
#> pred_56 -0.1911821164 0.534963417 0.361137259
#> pred_57 -0.2675014606 0.254309947 0.279822734
#> pred_58 -0.2137190764 0.407279227 0.342840554
#> pred_59 -0.2495973465 0.379055875 0.298292855
#> pred_510 -0.2810973573 0.381675135 0.277335943
#> pred_511 -0.1739886449 0.381345755 0.370597794
#> pred_512 -0.2890106853 0.439796268 0.269470771
#> pred_513 -0.2391290552 0.332125581 0.305599936
#> pred_514 -0.2196325644 0.271431497 0.332254406
#> pred_515 -0.2953699988 0.248911315 0.254830677
#> pred_6 -0.0025253173 0.009330518 0.016995500