predict.bigglm: Predictions from a biglm/bigglm

In biglm: Bounded Memory Linear and Generalized Linear Models

Description

Computes fitted means and standard errors at new data values after fitting a model with biglm or bigglm.

Usage

## S3 method for class 'bigglm'
predict(object, newdata, type = c("link", "response"), 
se.fit = FALSE, make.function = FALSE, ...)
## S3 method for class 'biglm'
predict(object, newdata=NULL,  se.fit = FALSE, make.function = FALSE, ...)

Arguments

object	fitted model
newdata	data frame with variables for new values
type	link is on the linear predictor scale, response is the response
se.fit	Compute standard errors?
make.function	If TRUE return a prediction function, see Details below
...	not used

Details

When make.function is TRUE, the return value is either a single function that computes the fitted values or a list of two functions that compute the fitted values and standard errors. The input to these functions is the design matrix, without the intercept column. This allows the relatively time-consuming calls to model.frame() and model.matrix() to be avoided.

Value

Either a vector of predicted values or a data frame with predicted values and standard errors.

Author(s)

based on code by Christophe Dutang

References

~put references to the literature/web site here ~

See Also

predict.glm,biglm,bigglm

Examples

example(biglm)
predict(a,newdata=trees)
f<-predict(a,make.function=TRUE)
X<- with(trees, cbind(log(Girth),log(Height)))
f(X)

Example output

Loading required package: DBI

biglm> data(trees)

biglm> ff<-log(Volume)~log(Girth)+log(Height)

biglm> chunk1<-trees[1:10,]

biglm> chunk2<-trees[11:20,]

biglm> chunk3<-trees[21:31,]

biglm> a <- biglm(ff,chunk1)

biglm> a <- update(a,chunk2)

biglm> a <- update(a,chunk3)

biglm> summary(a)
Large data regression model: biglm(ff, chunk1)
Sample size =  31 
               Coef    (95%     CI)     SE p
(Intercept) -6.6316 -8.2312 -5.0320 0.7998 0
log(Girth)   1.9826  1.8326  2.1327 0.0750 0
log(Height)  1.1171  0.7082  1.5260 0.2044 0

biglm> deviance(a)
[1] 0.1854634

biglm> AIC(a)
[1] 48.18546
       [,1]
1  2.310270
2  2.297879
3  2.308547
4  2.807900
5  2.976888
6  3.022580
7  2.802931
8  2.945736
9  3.035777
10 2.981461
11 3.057130
12 3.031349
13 3.031349
14 2.974906
15 3.118250
16 3.246641
17 3.401459
18 3.475068
19 3.319702
20 3.218167
21 3.467691
22 3.524097
23 3.478455
24 3.643019
25 3.754853
26 3.929478
27 3.965974
28 3.983197
29 3.994242
30 3.994242
31 4.355446
          [,1]
 [1,] 2.310270
 [2,] 2.297879
 [3,] 2.308547
 [4,] 2.807900
 [5,] 2.976888
 [6,] 3.022580
 [7,] 2.802931
 [8,] 2.945736
 [9,] 3.035777
[10,] 2.981461
[11,] 3.057130
[12,] 3.031349
[13,] 3.031349
[14,] 2.974906
[15,] 3.118250
[16,] 3.246641
[17,] 3.401459
[18,] 3.475068
[19,] 3.319702
[20,] 3.218167
[21,] 3.467691
[22,] 3.524097
[23,] 3.478455
[24,] 3.643019
[25,] 3.754853
[26,] 3.929478
[27,] 3.965974
[28,] 3.983197
[29,] 3.994242
[30,] 3.994242
[31,] 4.355446

biglm documentation built on Nov. 27, 2020, 5:08 p.m.