scidb (version 1.2-0)

predict: Prediction for SciDB GLM fits

Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized linear model object.

Usage

"predict"(object, ...)

Arguments

object

a glm_scidb model object.

...

optional arguments newdata, type, and se.fit (see details).

Value

If se.fit = FALSE, a vector of predictions.If se.fit = TRUE, a list with components

fit

Predictions, as for se.fit = FALSE.

se.fit

Estimated standard errors.

residual.scale

A scalar giving the square root of the dispersion used in computing the standard errors.

Details

Optional arguments:

• newdata a scidbdf SciDB data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
• type the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities.
• se.fit logical switch indicating if standard errors are required.

See Also

scidb glm

Examples

## Not run: 
# ## example adpted from Venables and Ripley (2002, pp. 190-2.):
# 
# # In R:
# ldose   <- rep(0:5, 2)
# numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
# sex     <- factor(rep(c("M", "F"), c(6, 6)))
# data    <- data.frame(sex, ldose)
# data    <- Reduce(rbind, 
#              lapply(1:length(numdead),
#                function(j) rbind(cbind(alive=1,data[j,])[rep(1,numdead[j]),],
#                                  cbind(alive=0,data[j,])[rep(1,20-numdead[j]),])))
# rownames(data) <- NULL
# 
# r_model <- glm( alive ~ sex + ldose - 1, family=binomial(), data=data)
# 
# # Now in SciDB:
# data_scidb <- as.scidb(data)
# str(data_scidb)
# scidb_model <- glm( alive ~ sex + ldose - 1, family=binomial(), data=data_scidb)
# 
# # New data for prediction:
# ld <- seq(0,5,0.1)
# newdata <- as.scidb(data.frame(ldose=ld, sex=rep("M",length(ld))))
# head(newdata)
# 
# pred_scidb = predict(scidb_model, newdata=newdata, type="response")
# head(pred_scidb)
# 
# require("graphics")
# plot(c(1,32), c(0,1), type = "n", xlab = "dose",
#           ylab = "prob", log = "x")
# text(2^ldose, numdead/20, as.character(sex))
# lines(2^ld, pred_scidb[],lwd=2,col=4)
# ## End(Not run)