predict.gsi: A function to predict multivariate output

Description Usage Arguments Details Value See Also Examples

Description

The function predict.gsi generates predicted multivariate output for user-specified combinations of levels of the input factors.

Usage

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## S3 method for class 'gsi'
predict(object, newdata, ...)

Arguments

object

Object of class gsi.

newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. need to be same factors and levels as for obtained the gsi object.

...

others parameters

Details

Only available if the gsi object was obtained with analysis.anoasg and analysis.args$keep.outputs=TRUE.

Value

a data.frame of predicted values for newdata

See Also

gsi, multisensi, analysis.anoasg

Examples

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  data(biomasseX)
  data(biomasseY)
  x=multisensi(design=biomasseX,model=biomasseY,basis=basis.ACP,
               analysis=analysis.anoasg,
               analysis.args=list(formula=2, keep.outputs=TRUE))
  newdata=as.data.frame(apply(biomasseX,2,unique))
  predict(x,newdata)

Example output

[*] Dimension Reduction 
[*] Analysis + Sensitivity Indices 
[*] Goodness of fit computing 
            Y10        Y20       Y30       Y40       Y50       Y60        Y70
[1,]  0.1252511  0.5515546  1.664603  3.262706  5.183573  7.265539 11.5204584
[2,]  0.0915475  0.4639582  1.659556  3.730320  6.548417 10.496329 20.5198688
[3,] -0.1812535 -0.7075716 -1.764164 -2.787954 -3.560857 -3.135364  0.6087779
           Y80      Y90     Y100      Y110      Y120     Y130     Y140     Y150
[1,] 17.267211 25.00869 32.97684  51.46193  73.42998 116.6996 153.8485 199.1266
[2,] 36.155398 61.36540 91.17915 162.19081 244.99189 388.1938 501.7553 641.9459
[3,]  8.911531 27.16796 54.50055 129.64672 225.85216 401.1542 544.3384 725.1009
         Y160      Y170      Y180      Y190      Y200      Y210      Y220
[1,] 253.6502  322.0202  385.4728  443.3714  503.0744  562.8465  618.6281
[2,] 816.6826 1058.1355 1292.1317 1514.9073 1745.8522 1987.1443 2216.4508
[3,] 954.5670 1279.8871 1597.8888 1906.1419 2231.7531 2578.1131 2917.2632

multisensi documentation built on May 2, 2019, 2:14 p.m.