| drglm {drglm} | R Documentation |
Fitting Linear and Generalized Linear Model in "Divide and Recombine" approach to Large Data Sets
Description
Function drglm aimed to fit GLMs to datasets larger in size that can be stored in memory. It uses popular divide and recombine technique to handle large data sets efficiently.Function drglm optimizes performance when linked with optimized BLAS libraries like ATLAS.The function drglm requires defining the number of chunks K and the fitfunction.The rest of the arguments are almost identical with the speedglm or biglm package.
Usage
drglm(formula, family, data, k, fitfunction)
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. |
family |
An explanation of the error distribution that will be implemented in the model. |
data |
A data frame, list, or environment that is not required but can be provided if available. |
k |
Number of subsets to be used. |
fitfunction |
The function to be utilized for model fitting. |
Value
A Generalized Linear Model is fitted in "Divide & Recombine" approach using "k" chunks to data set. A list of model coefficients is estimated using divide and recombine method with the respective standard error of estimates.
Author(s)
MH Nayem
References
Xi, R., Lin, N., & Chen, Y. (2009). Compression and aggregation for logistic regression analysis in data cubes. IEEE Transactions on Knowledge and Data Engineering, 21(4).
Chen, Y., Dong, G., Han, J., Pei, J., Wah, B. W., & Wang, J. (2006). Regression cubes with lossless compression and aggregation. IEEE Transactions on Knowledge and Data Engineering, 18(12).
Zuo, W., & Li, Y. (2018). A New Stochastic Restricted Liu Estimator for the Logistic Regression Model. Open Journal of Statistics, 08(01).
Karim, M. R., & Islam, M. A. (2019). Reliability and Survival Analysis. In Reliability and Survival Analysis.
Enea, M. (2009) Fitting Linear Models and Generalized Linear Models with large data sets in R.
Bates, D. (2009) Technical Report on Least Square Calculations.
Lumley, T. (2009) biglm package documentation.
See Also
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 MLRM
nmodel= drglm::drglm(pred_1 ~ pred_2+ pred_3+ pred_4+ pred_5+ pred_6,
data=dataset, family="gaussian", fitfunction="speedglm", k=10)
#Output
nmodel
#fitting simple logistic regression model
bmodel=drglm::drglm(pred_3~ pred_1+ pred_2+ pred_4+ pred_5+ pred_6,
data=dataset, family="binomial", fitfunction="speedglm", k=10)
#Output
bmodel
#fitting poisson regression model
pmodel=drglm::drglm(pred_5~ pred_1+ pred_2+ pred_3+ pred_4+ pred_6,
data=dataset, family="binomial", fitfunction="speedglm", k=10)
#Output
pmodel
#fitting multinomial logistic regression model
mmodel=drglm::drglm(pred_4~ pred_1+ pred_2+ pred_3+ pred_5+ pred_6,
data=dataset, family="multinomial", fitfunction="multinom", k=10)
#Output
mmodel