Description Usage Arguments Value Constraints See Also Examples
steps
performs linear regression on FLTable objects.
Choose a formula based model by p-values and R-Squared Values.
1 |
object |
An object of class FLTable |
scope |
A symbolic description of model to be fitted.
|
scale |
currently not used. |
direction |
character.Must be one of backward, Fbackward,UFbackward,forward. |
trace |
if positive, information is printed out during the running of the steps. |
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. |
highestpAllow1 |
All the variables whose p-value exceed the value specified by HighestpAllow1 are dropped in one go. Typical value for HighestProbAllow1 could be 0.50. Must be >0 and < 1. Not applicable for forward. |
highestpAllow2 |
Only one variable is dropped at a time till all the p-Values are below the HighestpAllow2. Typical value could be 0.10. Must be >0 and < 1. Not applicable for forward and backward. |
stepWiseDecrease |
The StepwiseDecrease is used to decrease the p-Value at each stage. In first step, all variables having pValue exceeding HighestpValue1 are dropped. Then the HighestpValue1 is reduced by StepwiseDecreasepValue and the process is repeated until all the variables have p-value less than HighestpValue2. Must be >0 and <1. Used only for UFbackward. |
step
performs linear regression and replicates equivalent R output.
The anova method is not yet available for FLLinRegr.
Properties like print(fit$x),model,plot
might take time as they
have to fetch data
step
for R reference implementation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | widetable <- FLTable(getTestTableName("tblAbaloneWide"), "ObsID")
s <- step(widetable,
scope=list(lower=Rings~Height+Diameter),
direction = "UFbackward")
plot(s)
s$coefficients
s <- step(widetable,
scope=list(lower=Rings~Height+Diameter,
upper=Rings~Height+Diameter+Sex+Num_Length),
direction = "UFbackward")
plot(s)
s$coefficients
s <- step(widetable,
scope=list(lower=Rings~Num_Length),
direction = "UFbackward",
performNorm=1,performVarReduc=1,maxCorrel=0.6)
plot(s)
s$coefficients
s <- step(widetable,
scope=list(upper=Rings~Height+Diameter+Sex+Num_Length+DummyCat),
direction = "Fbackward")
plot(s)
s$coefficients
s <- step(widetable,
scope=Rings~Height+Diameter+Sex+Num_Length+DummyCat,
direction = "forward")
plot(s)
s$coefficients
s <- step(widetable,
scope=Rings~Height+Diameter+Sex+Num_Length+DummyCat,
direction = "Fbackward")
plot(s)
s$coefficients
s <- step(widetable,
scope=list(upper=Rings~Height+Diameter+Sex+Num_Length+DummyCat),
direction = "forward")
plot(s)
s$coefficients
deeptable <- FLTable(getTestTableName("myLinRegrSmall"),"ObsID","VarID","Num_Val")
s <- step(deeptable,
scope=list(upper=c("-1","0","1")),
direction = "backward")
s <- step(deeptable,
scope=list(upper=c("1","2"),lower=c("1")),
direction = "Fbackward")
s <- step(deeptable,
scope=list(lower=c("2")),
direction = "UFbackward")
s <- step(deeptable,
scope=list(),
direction = "forward")
deeptable1 <- FLTable(getTestTableName("tblLogRegr"),
"ObsID","VarID","Num_Val",
whereconditions=c("ObsID < 7001","VarID<5"))
s <- step(deeptable1,
scope=list(lower=c("2")),
direction = "UFbackward",familytype = "logistic")
s <- step(deeptable1,
scope=list(),
direction = "forward",familytype="logistic")
plot(s)
s <- step(deeptable1,
scope=list(upper=c("-1","0","1","2","3")),
direction = "backward",
familytype="multinomial",pRefLevel=1)
s <- step(deeptable1,
scope=list(upper=c("1","2","3"),lower=c("2")),
direction = "Fbackward",familytype="multinomial",pRefLevel=1)
deeptable2 <- FLTable(getTestTableName("tblLogRegrMN10000"),
"ObsID","VarID","Num_Val",
whereconditions=c("ObsID < 7001","VarID<5"))
s <- step(deeptable2,
scope=list(lower=c("2")),
direction = "UFbackward",familytype = "multinomial",pRefLevel=1)
summary(s)
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