**See also:** Bigamy Bigamist Big Beglamored Bigot Bigly Begging Biggest Bigger Boggle Biggin Biggie Bigoted Bigotry Biggens Biggering Bigg Biggy Biggo Bigged

**1.** ** Bigglm** creates a generalized linear model object that uses only p^2 memory for p variables.

Bigglm

**2.** The *Bigglm*.data.frame method gives an example of how such a function might be written, another is in the Examples below

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**3.** It returns an object of class** " Bigglm"** that inherits from class "glmnet"

Bigglm

**4.** Description** Bigglm** creates a generalized linear model object that uses only p^2 memory for p variables.

Bigglm

**5.** ## ## Make a linear model using** biglm** ## require(biglm) mymodel - *Bigglm*(payment ~ sex + age + place.served, data = x) summary(mymodel) # This will overflow your RAM as it will get your data from ff into RAM #summary(glm(payment ~ sex + age + place.served, data = x[,c("payment","sex","age","place.served")]))

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**6.** *Bigglm*.ffdf(formula, data, family = gaussian(),, where formula is something like Y~X, assuming Y and X correspond to the colnames of ffdf object called data

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**7.** ** Bigglm** on your big data set in open source R, it just works

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**8.** And *Bigglm*.big.matrix() functions;“biglm”stands for“bounded memory linear regression.” In this example, the movie release year is used (as a factor) to try to predict customer ratings: > lm.0 = biglm.big.matrix(rating ~ year, data = x, fc = "year")

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**9.** ** Bigglm** does not provide a mechanism for setting factor levels on the fly

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**10.** Model selection of biglm::** Bigglm** models is not so straightforward

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**11.** Description *Bigglm*.ffdf creates a generalized linear model object that uses only p^2 memory for p variables

Bigglm

**12.** System.time(** Bigglm**(DepDelay~DayOfWeek+DepTime+CRSDepTime+ArrTime+CRSArrTime+UniqueCarrier, data=x)) # user system elapsed # 70.087 15.587 103.662 Now wasn’t that a lot better

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**13.** Using the ** Bigglm**() function we got results about 23 times faster.

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**14.** A biglm object created by a call to biglm::biglm() or biglm::** Bigglm**()

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**15.** I am simulating data and comparing glm.fit , ** Bigglm**, speedglm, glmnet, LiblineaR for binary logit model

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**16.** 0.9 fix ODBC and DBI interfaces for ** Bigglm** to not use LIMIT, and just not allow variables to be floating free in the workspace (which really couldn't work anyway) fix arguably-false-positive from Fortran bounds checking, by incorporating the fix in the published AS274 0.8 allow offsets in model formulas for both biglm and

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**17.** Biglm and ** Bigglm** (chunked fitting with package biglm) Bootstrapping (chunked and parallelized random access) Bagged predictive modelling (chunked and parallelized random access) Bagged clustering (chunked and parallelized random access with truecluster) Likelihood maximization (chunked and parallelized sequential access)

Biglm, Bigglm, Bootstrapping, Bagged

**18.** ** Bigglm** creates a generalized linear model object that uses only p^2 memory for p variables.: 2

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**19.** ** Bigglm** 7

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**20.** Usage ** Bigglm**(x, , path = FALSE) Arguments x input matrix Most other arguments to glmnet that make sense

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**21.** According to the documentation trail, *Bigglm* () is based on Alan Miller’s 1991 refinement (algorithm AS 274 implemented in Fortran 77) to W

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**22.** Library (ffbase) library (biglm) library (ff) data (trees) x <- as.ffdf (trees) a <- *Bigglm*.ffdf (log (Volume)~log (Girth)+log (Height), data=x, chunksize=10, sandwich=TRUE)

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**23.** *Bigglm* does not provide a mechanism for setting factor levels on the fly

Bigglm

**24.** A biglm object created by a call to biglm::biglm() or biglm::** Bigglm**()

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**25.** ** Bigglm**() fit a glm with all the options in glmnet

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**26.** \ code {\ link [biglm: ** Bigglm**]{biglm::

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**27.** > lmRDemo <-** Bigglm**(Id~x1+x2,data=airpoll) >summary(lmRDemo) Large data regression model:

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**28.** The ** Bigglm** function came later and the models other than Gaussian require multiple passes through the data so instead of the update mechanism that biglm uses,

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**29.** Faster than ** Bigglm** or other big data functions in R

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**30.** System.time(** Bigglm**(DepDelay~DayOfWeek+DepTime+CRSDepTime+ArrTime+CRSArrTime+UniqueCarrier, data=x)) # user system elapsed # 70.087 15.587 103.662 Now wasn’t that a lot better

Bigglm, Better

**31.** Using the ** Bigglm**() function we got results about 23 times faster.

Bigglm

**32.**

Up to**$5**cash back

Back, Bigglm

**33.** To do this, you would open a database connection using RODBC or RSQLite and then call ** Bigglm** with the data argument specifying the database connection and tablename specifying the …

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**34.** ** Bigglm**.big.matrix, bigkmeans, binit, and applyfor big.matrixobjects

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**35.** ** Bigglm**() , from the package biglm by Thomas Lumley

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**36.** The ** Bigglm** function in the biglm package does the iteration using bounded memory, by reading in the data in chunks, and starting again at the beginning for each iteration

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**37.** ** Bigglm** iterations If p is not too large and the data are reasonably well-behaved so that the loglikelihood is well-approximated by a quadratic, three iterations should be suﬃcient and good starting values will cut this to two iterations or even to one

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**38.** Value It returns an object of class "** Bigglm**" that inherits from class "glmnet"

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**39.** 내가 데이터를 시뮬레이션 이진 로짓 모델 glm.fit, ** Bigglm**, speedglm, glmnet, LiblineaR을 비교하고를 사용하여 로지스틱 회귀 분석을 벤치마킹.

Bigglm

**BIGGLM**