Description Usage Arguments Details Value Constraints See Also Examples
lm
performs linear regression on FLTable objects.
1 |
formula |
A symbolic description of model to be fitted |
data |
An object of class FLTable or FLTableMD |
catToDummy |
Transform categorical variables to numerical values either using dummy variables or by using Empirical Logit. If the value is 1, transformation is done using dummy variables, else if the value is 0, transformation is done using Empirical Logit. |
performNorm |
0/1 indicating whether to perform standardization of data. |
performVarReduc |
0/1. If the value is 1, the stored procedure eliminates variables based on standard deviation and correlation. |
makeDataSparse |
If 0,Retains zeroes and NULL values from the input table. If 1, Removes zeroes and NULL. If 2,Removes zeroes but retains NULL values. |
minStdDev |
Minimum acceptable standard deviation for elimination of variables. Any variable that has a standard deviation below this threshold is eliminated. This parameter is only consequential if the parameter PerformVarReduc = 1. Must be >0. |
maxCorrel |
Maximum acceptable absolute correlation between a pair of columns for eliminating variables. If the absolute value of the correlation exceeds this threshold, one of the columns is not transformed. Again, this parameter is only consequential if the parameter PerformVarReduc = 1. Must be >0 and <=1. |
classSpec |
list describing the categorical dummy variables. |
whereconditions |
takes the where_clause as a string. |
The DB Lytix function called is FLLinRegr. Performs Linear Regression and stores the results in predefined tables.
lm
returns an object of class FLLinRegr
The anova method is not yet available for FLLinRegr
If data
is FLTableMD, only single formula is accepted.
So input deeptable or deeptable produced after data preparation
should have same VarIDs'.
For FLTableMD data object, only coefficients and summary
methods are defined.Predict method on FLTableMD
newdata
is not supported.
Properties like print(x),model,plot
might take time as they
have to fetch data
lm
for R reference implementation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | widetable <- FLTable(getTestTableName("tblAbaloneWide"), "ObsID")
lmfit <- lm(Rings~Height+Diameter,widetable)
lmfit$coefficients
lmfit$fitted.values
plot(lmfit)
mu <- predict(lmfit,newdata=widetable)
deeptable <- FLTable(getTestTableName("myLinRegrSmall"),"ObsID","VarID","Num_Val")
lmfit <- lm(NULL,deeptable)
summary(lmfit)
flMDObject <- FLTableMD(table=getTestTableName("tblAutoMPGMD"),
group_id_colname="GroupID",
obs_id_colname="ObsID",group_id = c(2,4))
vformula <- MPG~HorsePower+Displacement+Weight+Acceleration
lmfit <- lm(vformula,
data=flMDObject)
coeffList <- coef(lmfit)
summaryList <- summary(lmfit)
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