Importance: Measure input importance (including sensitivity analysis)...
In rminer: Data Mining Classification and Regression Methods
Importance R Documentation
Measure input importance (including sensitivity analysis) given a supervised data mining model.
Description
Measure input importance (including sensitivity analysis) given a supervised data mining model.
Usage
Importance(M, data, RealL = 7, method = "1D-SA", measure = "AAD",
sampling = "regular", baseline = "mean", responses = TRUE,
outindex = NULL, task = "default", PRED = NULL,
interactions = NULL, Aggregation = -1, LRandom = -1,
MRandom = "discrete", Lfactor = FALSE)
Arguments
M
fitted model, typically is the object returned by fit.
Can also be any fitted model (i.e. not from rminer), provided that the predict function PRED is defined (see examples for details).
data
training data (the same data.frame that was used to fit the model, currently only used to add data histogram to VEC curve).
RealL
the number of sensitivity analysis levels (e.g. 7). Note: you need to use RealL>=2.
method
input importance method. Options are:
1D-SA – 1 dimensional sensitivity analysis, very fast, sets interactions to NULL.
sens or SA – sensitivity analysis. There are some extra variants:
sensa – equal to sens but also sets measure="AAD"; sensv – sets measure="variance"; sensg – sets measure="gradient"; sensr – sets measure="range". if interactions is not null, then GSA is assumed, else 1D-SA is assumed.
DSA – Data-based SA (good option if input interactions need to be detected).
MSA – Monte-Carlo SA.
CSA – Cluster-based SA.
GSA – Global SA (very slow method, particularly if the number of inputs is large, should be avoided).
randomForest – uses method of Leo Breiman (type=1), only makes sense when M is a randomRorest.
measure
sensitivity analysis measure (used to measure input importance). Options are:
AAD – average absolute deviation from the median.
gradient – average absolute gradient (y_i+1-y_i) of the responses.
variance – variance of the responses.
range – maximum - minimum of the responses.
sampling
for numeric inputs, the sampling scan function. Options are:
regular – regular sequence (uniform distribution), do not change this value, kept here only due to compatibility issues.
baseline
baseline vector used during the sensitivity analysis. Options are:
mean – uses a vector with the mean values of each attribute from data.
median – uses a vector with the median values of each attribute from data.
a data.frame with the baseline example (should have the same attribute names as data).
responses
if TRUE then all sensitivity analysis responses are stored and returned.
outindex
the output index (column) of data if M is not a model object (returned by fit).
task
the task as defined in fit if M is not a model object (returned by fit).
PRED
the prediction function of M, if M is not a model object (returned by fit).
Note: this function should behave like the rminer predict-methods, i.e. return a numeric vector in case of regression;
a matrix of examples (rows) vs probabilities (columns) (task="prob") or a factor (task="class") in case of classification.
interactions
numeric vector with the attributes (columns) used by Ith-D sensitivity analysis (2-D or higher, "GSA" method):
if NULL then only a 1-D sensitivity analysis is performed.
if length(interactions)==1 then a "special" 2-D sensitivity analysis is performed using the index of interactions versus
all remaining inputs. Note: the $sresponses[[interactions]] will be empty (in vecplot do not use xval =interactions).
if length(interactions)>1 then a full Ith-D sensitivity analysis is performed, where I=length(interactions).
Note: Computational effort can highly increase if I is too large, i.e. O(RealL^I). Also, you need to preprocess the
returned list (e.g. using avg_imp) to use the vecplot function (see the examples).
Aggregation
numeric value that sets the number of multi-metric aggregation function (used only for "DSA", ""). Options are:
-1 – the default value that should work in most cases (if regression, sets Aggregation=3, else if classification then sets Aggregation=1).
1 – value that should work for classification (only use the average of all sensitivity values).
3 – value that should work for regression (use 3 metrics, the minimum, average and maximum of all sensitivity values).
LRandom
number of samples used by DSA and MSA methods. The default value is -1, which means: use a number equal to training set size. If a different value is used (1<= value <= number of training samples), then LRandom samples are randomly selected.
MRandom
sampling type used by MSA: "discrete" (default discrete uniform distribution) or "continuous" (from continuous uniform distribution).
Lfactor
sets the maximum number of sensitivity levels for discrete inputs. if FALSE then a maximum of up to RealL levels are used (most frequent ones), else (TRUE) then all levels of the input are used in the SA analysis.
Details
This function provides several algorithms for measuring input importance of supervised data mining models and the average effect of a given input (or pair of inputs) in the model.
A particular emphasis is given on sensitivity analysis (SA), which is a simple method that measures the effects on the output of a given model
when the inputs are varied through their range of values. Check the references for more details.
Value
A list with the components:
$value – numeric vector with the computed sensitivity analysis measure for each attribute.
$imp – numeric vector with the relative importance for each attribute (only makes sense for 1-D analysis).
$sresponses – vector list as described in the Value documentation of mining.
$data – if DSA or MSA, store the used data samples, needed for visualizations made by vecplot.
$method – SA method
$measure – SA measure
$agg – Aggregation value
$nclasses – if task="prob" or "class", the number of output classes, else nclasses=1
$inputs – indexes of the input attributes
$Llevels – sensitivity levels used for each attribute (NA means output attribute)
$interactions – which attributes were interacted when method=GSA.
Note
See also http://www3.dsi.uminho.pt/pcortez/rminer.html
Author(s)
Paulo Cortez http://www3.dsi.uminho.pt/pcortez/
References
To cite the Importance function, sensitivity analysis methods or synthetic datasets, please use:
P. Cortez and M.J. Embrechts.
Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models.
In Information Sciences, Elsevier, 225:1-17, March 2013.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ins.2012.10.039")}
See Also
vecplot, fit, mining, mgraph, mmetric, savemining.
Examples
### dontrun is used when the execution of the example requires some computational effort.
### 1st example, regression, 1-D sensitivity analysis
## Not run:
data(sa_ssin) # x1 should account for 55
M=fit(y~.,sa_ssin,model="ksvm")
I=Importance(M,sa_ssin,method="1D-SA") # 1-D SA, AAD
print(round(I$imp,digits=2))
L=list(runs=1,sen=t(I$imp),sresponses=I$sresponses)
mgraph(L,graph="IMP",leg=names(sa_ssin),col="gray",Grid=10)
mgraph(L,graph="VEC",xval=1,Grid=10,data=sa_ssin,
main="VEC curve for x1 influence on y") # or:
vecplot(I,xval=1,Grid=10,data=sa_ssin,datacol="gray",
main="VEC curve for x1 influence on y") # same graph
vecplot(I,xval=c(1,2,3),pch=c(1,2,3),Grid=10,
leg=list(pos="bottomright",leg=c("x1","x2","x3"))) # all x1, x2 and x3 VEC curves
## End(Not run)
### 2nd example, regression, DSA sensitivity analysis:
## Not run:
I2=Importance(M,sa_ssin,method="DSA")
print(I2)
# influence of x1 and x2 over y
vecplot(I2,graph="VEC",xval=1) # VEC curve
vecplot(I2,graph="VECB",xval=1) # VEC curve with boxplots
vecplot(I2,graph="VEC3",xval=c(1,2)) # VEC surface
vecplot(I2,graph="VECC",xval=c(1,2)) # VEC contour
## End(Not run)
### 3th example, classification (pure class labels, task="cla"), DSA:
## Not run:
data(sa_int2_3c) # pair (x1,x2) is more relevant than x3, all x1,x2,x3 affect y,
# x4 has a null effect.
M2=fit(y~.,sa_int2_3c,model="mlpe",task="class")
I4=Importance(M2,sa_int2_3c,method="DSA")
# VEC curve (should present a kind of "saw" shape curve) for class B (TC=2):
vecplot(I4,graph="VEC",xval=2,cex=1.2,TC=2,
main="VEC curve for x2 influence on y (class B)",xlab="x2")
# same VEC curve but with boxplots:
vecplot(I4,graph="VECB",xval=2,cex=1.2,TC=2,
main="VEC curve with box plots for x2 influence on y (class B)",xlab="x2")
## End(Not run)
### 4th example, regression, DSA and GSA:
## Not run:
data(sa_psin)
# same model from Table 1 of the reference:
M3=fit(y~.,sa_psin,model="ksvm",search=2^-2,C=2^6.87,epsilon=2^-8)
# in this case: Aggregation should be -1 (default), 1 (class) or 3 (reg), see ref. paper.
I5=Importance(M3,sa_psin,method="DSA",Aggregation=3)
print("Input importances:")
print(round(I5$imp,digits=2)) # INS 2013 similar results
# 2D analysis (check reference for more details), RealL=L=7:
# need to aggregate results into a matrix of SA measure by using the agg_matrix_imp function.
# important notes:
# - agg_matrix_imp only works for the methods "DSA", "MSA" and "GSA".
# - reliable agg_matrix_imp results for "DSA" or "MSA" only for a
# a large LRandom value (e.g., LRandom=1000) or when LRandom=-1 (all training samples)
cm=agg_matrix_imp(I5)
print("show Table 8 DSA results (from the reference):")
print(round(cm$m1,digits=2))
print(round(cm$m2,digits=2))
# internal rminer function:
# show most relevant (darker) input pairs, in this case (x1,x2) > (x1,x3) > (x2,x3)
# to build a nice plot, a fixed threshold=c(0.05,0.05) is used. note that
# in the paper and for real data, we use threshold=0.1,
# which means threshold=rep(max(cm$m1,cm$m2)*threshold,2)
fcm=cmatrixplot(cm,threshold=c(0.05,0.05))
# 2D analysis using pair AT=c(x1,x2') (check reference for more details), RealL=7:
# nice 3D VEC surface plot:
vecplot(I5,xval=c(1,2),graph="VEC3",xlab="x1",ylab="x2",zoom=1.1,
main="VEC surface of (x1,x2') influence on y")
# same influence but know shown using VEC contour:
par(mar=c(4.0,4.0,1.0,0.3)) # change the graph window space size
vecplot(I5,xval=c(1,2),graph="VECC",xlab="x1",ylab="x2",
main="VEC surface of (x1,x2') influence on y")
# slower GSA:
I6=Importance(M3,sa_psin,method="GSA",interactions=1:4)
print("Input importances:")
print(round(I6$imp,digits=2)) # INS 2013 similar results
cm2=agg_matrix_imp(I6)
# compare cm2 with cm1, almost identical:
print(round(cm2$m1,digits=2))
print(round(cm2$m2,digits=2))
fcm2=cmatrixplot(cm2,threshold=0.1)
## End(Not run)
### 5th example, classification, 1D_SA, DSA, MSA and GSA:
## Not run:
data(sa_ssin_n2p)
# same model from Table 1 of the reference:
M4=fit(y~.,sa_ssin_n2p,model="ksvm",kpar=list(sigma=2^-8.25),C=2^10)
I7=Importance(M4,sa_ssin_n2p,method="1D-SA")
print("1D-SA Input importances:")
print(round(I7$imp,digits=2)) # INS 2013 similar results (Table 6)
I8=Importance(M4,sa_ssin_n2p,method="GSA",interactions=1:4)
print("GSA Input importances:")
print(round(I8$imp,digits=2)) # INS 2013 similar results (Table 6)
I9=Importance(M4,sa_ssin_n2p,method="DSA",LRandom=1000)
print("DSA Ns=1000 Input importances:")
print(round(I9$imp,digits=2)) # INS 2013 similar results (Table 6)
I10=Importance(M4,sa_ssin_n2p,method="DSA",LRandom=10)
print("DSA Ns=10 Input importances:")
print(round(I10$imp,digits=2)) # INS 2013 similar results (Table 6)
I11=Importance(M4,sa_ssin_n2p,method="MSA",LRandom=10)
print("MSA Ns=10 Input importances:")
print(round(I11$imp,digits=2)) # INS 2013 similar results (Table 6)
# 2D analysis:
cm3=agg_matrix_imp(I8)
fcm3=cmatrixplot(cm3,threshold=c(0.05,0.05))
cm4=agg_matrix_imp(I9)
fcm4=cmatrixplot(cm4,threshold=c(0.05,0.05))
## End(Not run)
### If you want to use Importance over your own model (different than rminer ones):
# 1st example, regression, uses the theoretical sin1reg function: x1=70% and x2=30%
data(sin1reg)
mypred=function(M,data)
{ return (M[1]*sin(pi*data[,1]/M[3])+M[2]*sin(pi*data[,2]/M[3])) }
M=c(0.7,0.3,2000)
# 4 is the column index of y
I=Importance(M,sin1reg,method="sens",measure="AAD",PRED=mypred,outindex=4)
print(I$imp) # x1=72.3% and x2=27.7%
L=list(runs=1,sen=t(I$imp),sresponses=I$sresponses)
mgraph(L,graph="IMP",leg=names(sin1reg),col="gray",Grid=10)
mgraph(L,graph="VEC",xval=1,Grid=10) # equal to:
par(mar=c(2.0,2.0,1.0,0.3)) # change the graph window space size
vecplot(I,graph="VEC",xval=1,Grid=10,main="VEC curve for x1 influence on y:")
### 2nd example, 3-class classification for iris and lda model:
## Not run:
data(iris)
library(MASS)
predlda=function(M,data) # the PRED function
{ return (predict(M,data)$posterior) }
LDA=lda(Species ~ .,iris, prior = c(1,1,1)/3)
# 4 is the column index of Species
I=Importance(LDA,iris,method="1D-SA",PRED=predlda,outindex=4)
vecplot(I,graph="VEC",xval=1,Grid=10,TC=1,
main="1-D VEC for Sepal.Lenght (x-axis) influence in setosa (prob.)")
## End(Not run)
### 3rd example, binary classification for setosa iris and lda model:
## Not run:
data(iris)
library(MASS)
iris2=iris;iris2$Species=factor(iris$Species=="setosa")
predlda2=function(M,data) # the PRED function
{ return (predict(M,data)$class) }
LDA2=lda(Species ~ .,iris2)
I=Importance(LDA2,iris2,method="1D-SA",PRED=predlda2,outindex=4)
vecplot(I,graph="VEC",xval=1,
main="1-D VEC for Sepal.Lenght (x-axis) influence in setosa (class)",Grid=10)
## End(Not run)
### Example with discrete inputs
## Not run:
data(iris)
ir1=iris
ir1[,1]=cut(ir1[,1],breaks=4)
ir1[,2]=cut(ir1[,2],breaks=4)
M=fit(Species~.,ir1,model="mlpe")
I=Importance(M,ir1,method="DSA")
# discrete example:
vecplot(I,graph="VEC",xval=1,TC=1,main="class: setosa (discrete x1)",data=ir1)
# continuous example:
vecplot(I,graph="VEC",xval=3,TC=1,main="class: setosa (cont. x1)",data=ir1)
## End(Not run)
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| Importance | R Documentation |
Measure input importance (including sensitivity analysis) given a supervised data mining model.
Description
Measure input importance (including sensitivity analysis) given a supervised data mining model.
Usage
Importance(M, data, RealL = 7, method = "1D-SA", measure = "AAD",
sampling = "regular", baseline = "mean", responses = TRUE,
outindex = NULL, task = "default", PRED = NULL,
interactions = NULL, Aggregation = -1, LRandom = -1,
MRandom = "discrete", Lfactor = FALSE)
Arguments
M |
fitted model, typically is the object returned by |
data |
training data (the same data.frame that was used to fit the model, currently only used to add data histogram to VEC curve). |
RealL |
the number of sensitivity analysis levels (e.g. 7). Note: you need to use |
method |
input importance method. Options are:
|
measure |
sensitivity analysis measure (used to measure input importance). Options are:
|
sampling |
for numeric inputs, the sampling scan function. Options are:
|
baseline |
baseline vector used during the sensitivity analysis. Options are:
|
responses |
if |
outindex |
the output index (column) of |
task |
the |
PRED |
the prediction function of |
interactions |
numeric vector with the attributes (columns) used by Ith-D sensitivity analysis (2-D or higher, "GSA" method):
|
Aggregation |
numeric value that sets the number of multi-metric aggregation function (used only for "DSA", ""). Options are:
|
LRandom |
number of samples used by DSA and MSA methods. The default value is -1, which means: use a number equal to training set size. If a different value is used (1<= value <= number of training samples), then LRandom samples are randomly selected. |
MRandom |
sampling type used by MSA: "discrete" (default discrete uniform distribution) or "continuous" (from continuous uniform distribution). |
Lfactor |
sets the maximum number of sensitivity levels for discrete inputs. if FALSE then a maximum of up to RealL levels are used (most frequent ones), else (TRUE) then all levels of the input are used in the SA analysis. |
Details
This function provides several algorithms for measuring input importance of supervised data mining models and the average effect of a given input (or pair of inputs) in the model. A particular emphasis is given on sensitivity analysis (SA), which is a simple method that measures the effects on the output of a given model when the inputs are varied through their range of values. Check the references for more details.
Value
A list with the components:
$value – numeric vector with the computed sensitivity analysis measure for each attribute.
$imp – numeric vector with the relative importance for each attribute (only makes sense for 1-D analysis).
$sresponses – vector list as described in the Value documentation of
mining.$data – if DSA or MSA, store the used data samples, needed for visualizations made by vecplot.
$method – SA method
$measure – SA measure
$agg – Aggregation value
$nclasses – if task="prob" or "class", the number of output classes, else nclasses=1
$inputs – indexes of the input attributes
$Llevels – sensitivity levels used for each attribute (NA means output attribute)
$interactions – which attributes were interacted when method=GSA.
Note
See also http://www3.dsi.uminho.pt/pcortez/rminer.html
Author(s)
Paulo Cortez http://www3.dsi.uminho.pt/pcortez/
References
To cite the Importance function, sensitivity analysis methods or synthetic datasets, please use:
P. Cortez and M.J. Embrechts.
Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models.
In Information Sciences, Elsevier, 225:1-17, March 2013.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ins.2012.10.039")}
See Also
vecplot, fit, mining, mgraph, mmetric, savemining.
Examples
### dontrun is used when the execution of the example requires some computational effort.
### 1st example, regression, 1-D sensitivity analysis
## Not run:
data(sa_ssin) # x1 should account for 55
M=fit(y~.,sa_ssin,model="ksvm")
I=Importance(M,sa_ssin,method="1D-SA") # 1-D SA, AAD
print(round(I$imp,digits=2))
L=list(runs=1,sen=t(I$imp),sresponses=I$sresponses)
mgraph(L,graph="IMP",leg=names(sa_ssin),col="gray",Grid=10)
mgraph(L,graph="VEC",xval=1,Grid=10,data=sa_ssin,
main="VEC curve for x1 influence on y") # or:
vecplot(I,xval=1,Grid=10,data=sa_ssin,datacol="gray",
main="VEC curve for x1 influence on y") # same graph
vecplot(I,xval=c(1,2,3),pch=c(1,2,3),Grid=10,
leg=list(pos="bottomright",leg=c("x1","x2","x3"))) # all x1, x2 and x3 VEC curves
## End(Not run)
### 2nd example, regression, DSA sensitivity analysis:
## Not run:
I2=Importance(M,sa_ssin,method="DSA")
print(I2)
# influence of x1 and x2 over y
vecplot(I2,graph="VEC",xval=1) # VEC curve
vecplot(I2,graph="VECB",xval=1) # VEC curve with boxplots
vecplot(I2,graph="VEC3",xval=c(1,2)) # VEC surface
vecplot(I2,graph="VECC",xval=c(1,2)) # VEC contour
## End(Not run)
### 3th example, classification (pure class labels, task="cla"), DSA:
## Not run:
data(sa_int2_3c) # pair (x1,x2) is more relevant than x3, all x1,x2,x3 affect y,
# x4 has a null effect.
M2=fit(y~.,sa_int2_3c,model="mlpe",task="class")
I4=Importance(M2,sa_int2_3c,method="DSA")
# VEC curve (should present a kind of "saw" shape curve) for class B (TC=2):
vecplot(I4,graph="VEC",xval=2,cex=1.2,TC=2,
main="VEC curve for x2 influence on y (class B)",xlab="x2")
# same VEC curve but with boxplots:
vecplot(I4,graph="VECB",xval=2,cex=1.2,TC=2,
main="VEC curve with box plots for x2 influence on y (class B)",xlab="x2")
## End(Not run)
### 4th example, regression, DSA and GSA:
## Not run:
data(sa_psin)
# same model from Table 1 of the reference:
M3=fit(y~.,sa_psin,model="ksvm",search=2^-2,C=2^6.87,epsilon=2^-8)
# in this case: Aggregation should be -1 (default), 1 (class) or 3 (reg), see ref. paper.
I5=Importance(M3,sa_psin,method="DSA",Aggregation=3)
print("Input importances:")
print(round(I5$imp,digits=2)) # INS 2013 similar results
# 2D analysis (check reference for more details), RealL=L=7:
# need to aggregate results into a matrix of SA measure by using the agg_matrix_imp function.
# important notes:
# - agg_matrix_imp only works for the methods "DSA", "MSA" and "GSA".
# - reliable agg_matrix_imp results for "DSA" or "MSA" only for a
# a large LRandom value (e.g., LRandom=1000) or when LRandom=-1 (all training samples)
cm=agg_matrix_imp(I5)
print("show Table 8 DSA results (from the reference):")
print(round(cm$m1,digits=2))
print(round(cm$m2,digits=2))
# internal rminer function:
# show most relevant (darker) input pairs, in this case (x1,x2) > (x1,x3) > (x2,x3)
# to build a nice plot, a fixed threshold=c(0.05,0.05) is used. note that
# in the paper and for real data, we use threshold=0.1,
# which means threshold=rep(max(cm$m1,cm$m2)*threshold,2)
fcm=cmatrixplot(cm,threshold=c(0.05,0.05))
# 2D analysis using pair AT=c(x1,x2') (check reference for more details), RealL=7:
# nice 3D VEC surface plot:
vecplot(I5,xval=c(1,2),graph="VEC3",xlab="x1",ylab="x2",zoom=1.1,
main="VEC surface of (x1,x2') influence on y")
# same influence but know shown using VEC contour:
par(mar=c(4.0,4.0,1.0,0.3)) # change the graph window space size
vecplot(I5,xval=c(1,2),graph="VECC",xlab="x1",ylab="x2",
main="VEC surface of (x1,x2') influence on y")
# slower GSA:
I6=Importance(M3,sa_psin,method="GSA",interactions=1:4)
print("Input importances:")
print(round(I6$imp,digits=2)) # INS 2013 similar results
cm2=agg_matrix_imp(I6)
# compare cm2 with cm1, almost identical:
print(round(cm2$m1,digits=2))
print(round(cm2$m2,digits=2))
fcm2=cmatrixplot(cm2,threshold=0.1)
## End(Not run)
### 5th example, classification, 1D_SA, DSA, MSA and GSA:
## Not run:
data(sa_ssin_n2p)
# same model from Table 1 of the reference:
M4=fit(y~.,sa_ssin_n2p,model="ksvm",kpar=list(sigma=2^-8.25),C=2^10)
I7=Importance(M4,sa_ssin_n2p,method="1D-SA")
print("1D-SA Input importances:")
print(round(I7$imp,digits=2)) # INS 2013 similar results (Table 6)
I8=Importance(M4,sa_ssin_n2p,method="GSA",interactions=1:4)
print("GSA Input importances:")
print(round(I8$imp,digits=2)) # INS 2013 similar results (Table 6)
I9=Importance(M4,sa_ssin_n2p,method="DSA",LRandom=1000)
print("DSA Ns=1000 Input importances:")
print(round(I9$imp,digits=2)) # INS 2013 similar results (Table 6)
I10=Importance(M4,sa_ssin_n2p,method="DSA",LRandom=10)
print("DSA Ns=10 Input importances:")
print(round(I10$imp,digits=2)) # INS 2013 similar results (Table 6)
I11=Importance(M4,sa_ssin_n2p,method="MSA",LRandom=10)
print("MSA Ns=10 Input importances:")
print(round(I11$imp,digits=2)) # INS 2013 similar results (Table 6)
# 2D analysis:
cm3=agg_matrix_imp(I8)
fcm3=cmatrixplot(cm3,threshold=c(0.05,0.05))
cm4=agg_matrix_imp(I9)
fcm4=cmatrixplot(cm4,threshold=c(0.05,0.05))
## End(Not run)
### If you want to use Importance over your own model (different than rminer ones):
# 1st example, regression, uses the theoretical sin1reg function: x1=70% and x2=30%
data(sin1reg)
mypred=function(M,data)
{ return (M[1]*sin(pi*data[,1]/M[3])+M[2]*sin(pi*data[,2]/M[3])) }
M=c(0.7,0.3,2000)
# 4 is the column index of y
I=Importance(M,sin1reg,method="sens",measure="AAD",PRED=mypred,outindex=4)
print(I$imp) # x1=72.3% and x2=27.7%
L=list(runs=1,sen=t(I$imp),sresponses=I$sresponses)
mgraph(L,graph="IMP",leg=names(sin1reg),col="gray",Grid=10)
mgraph(L,graph="VEC",xval=1,Grid=10) # equal to:
par(mar=c(2.0,2.0,1.0,0.3)) # change the graph window space size
vecplot(I,graph="VEC",xval=1,Grid=10,main="VEC curve for x1 influence on y:")
### 2nd example, 3-class classification for iris and lda model:
## Not run:
data(iris)
library(MASS)
predlda=function(M,data) # the PRED function
{ return (predict(M,data)$posterior) }
LDA=lda(Species ~ .,iris, prior = c(1,1,1)/3)
# 4 is the column index of Species
I=Importance(LDA,iris,method="1D-SA",PRED=predlda,outindex=4)
vecplot(I,graph="VEC",xval=1,Grid=10,TC=1,
main="1-D VEC for Sepal.Lenght (x-axis) influence in setosa (prob.)")
## End(Not run)
### 3rd example, binary classification for setosa iris and lda model:
## Not run:
data(iris)
library(MASS)
iris2=iris;iris2$Species=factor(iris$Species=="setosa")
predlda2=function(M,data) # the PRED function
{ return (predict(M,data)$class) }
LDA2=lda(Species ~ .,iris2)
I=Importance(LDA2,iris2,method="1D-SA",PRED=predlda2,outindex=4)
vecplot(I,graph="VEC",xval=1,
main="1-D VEC for Sepal.Lenght (x-axis) influence in setosa (class)",Grid=10)
## End(Not run)
### Example with discrete inputs
## Not run:
data(iris)
ir1=iris
ir1[,1]=cut(ir1[,1],breaks=4)
ir1[,2]=cut(ir1[,2],breaks=4)
M=fit(Species~.,ir1,model="mlpe")
I=Importance(M,ir1,method="DSA")
# discrete example:
vecplot(I,graph="VEC",xval=1,TC=1,main="class: setosa (discrete x1)",data=ir1)
# continuous example:
vecplot(I,graph="VEC",xval=3,TC=1,main="class: setosa (cont. x1)",data=ir1)
## End(Not run)
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