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#' @demoTitle baseball-lm
#'
#' Demo linear regression model (lm)
#'
#' To install and use baseball demo dataset in Aster:
#'
#' 1. download baseball.zip from
#' https://bitbucket.org/grigory/toaster/downloads/baseball.zip
#' 2. run script to create data set in Aster
#' sh load_baseball_data.sh -d mydbname -U username -w mypassword
#' 3. create Aster ODBC DSN on your desktop
#' see https://bitbucket.org/grigory/toaster/wiki/Home#markdown-header-odbc-driver-and-dns
library(toaster)
## utility input function
readlineDef <- function(prompt, default) {
if (!is.null(prompt))
prompt = paste0(prompt, "[", default, "]: ")
else
prompt = paste0(prompt, ": ")
result = readline(prompt)
if (result == "")
return (default)
else
return (result)
}
## utility connection functions
connectAdHocToAster <- function(uid="beehive", pwd=NULL, server="localhost", port="2406", database="beehive") {
uid = readlineDef("Enter user id: ", uid)
pwd = readlineDef("Enter password: ", pwd)
server = readlineDef("Enter server: ", server)
port = readlineDef("Enter port: ", port)
database = readlineDef("Enter database: ", database)
tryCatch(close(conn), error=function(err) {NULL})
connectStr = paste0("driver={Aster ODBC Driver};server=", server, ";port=", port,
";database=",database,";uid=",uid,";pwd=", pwd)
conn = tryCatch({
conn = odbcDriverConnect(connection=connectStr)
odbcGetInfo(conn)
return (conn)
}, error=function(err) {
stop("Can't connect to Aster - check ip/port/database/uid/pwd", err)
})
}
connectWithDSNToAster <- function(dsn=NULL) {
dsn = readlineDef("Enter Aster ODBC DSN: ", dsn)
tryCatch(close(conn), error=function(err) {NULL})
conn = tryCatch({
conn = odbcConnect(dsn)
odbcGetInfo(conn)
return (conn)
}, error=function(err) {
stop(paste("Can't connect to Aster - check DSN:", dsn))
})
}
pause <- function() {
cat("Press ENTER/RETURN/NEWLINE to continue.")
readLines(n=1)
invisible()
}
### connect first
conn = connectWithDSNToAster()
## must be connected to baseball dataset
if(!all(isTable(conn, c('pitching_enh', 'batting_enh')))) {
stop("Must connect to baseball dataset and tables must exist.")
}
### simple model with 3 numerical predictors
model1 = computeLm(channel=conn, tableName="batting_enh",
formula= ba ~ rbi + bb + so, sampleSize=10000)
summary(model1)
plot(model1)
### compare with lm()
data = computeSample(channel=conn, tableName="batting_enh",
include=c("ba","rbi","bb","so"), sampleSize=10000)
fit1 = lm(formula= ba ~ rbi + bb + so, data=data)
summary(fit1)
plot(fit1)
pause()
### added ER predictor to the model and defined subset with where
modelNL = computeLm(channel=conn, tableName="pitching_enh", formula= era ~ er + hr + bb + so,
where = "yearid >= 2000 and lgid = 'NL'")
summary(modelNL)
plot(modelNL)
pause()
### added categorical predictor
modelLg = computeLm(channel=conn, tableName="batting_enh", formula=ba ~ rbi + bb + so + lgid,
where="lgid in ('AL','NL')")
summary(modelLg)
pause()
### category with more than 2 values, also lowered sample size to 500 rows
### (coefficients are always computed on all data)
modelTeam10K = computeLm(channel=conn, tableName="batting_enh", formula=ba ~ rbi + bb + so + teamid,
sampleSize = 50, where="teamid in ('TEX','NYY','OAK','PIT','DET') and yearid >= 1990")
summary(modelTeam10K)
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