Package 'RSDA'

Description

$ operator for histograms

Usage

## S3 method for class 'symbolic_histogram'
x$name

Arguments

Description

$ operator for modals

Usage

## S3 method for class 'symbolic_modal'
x$name = c("cats", "props", "counts")

Arguments

Description

$ operator for set

Usage

## S3 method for class 'symbolic_set'
x$name = c("levels", "values")

Arguments

Description

Example of SODAS XML data file converted in a CSV file in RSDA format.

Usage

data(abalone)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 24 rows and 7 columns.

Source

http://www.info.fundp.ac.be/asso/sodaslink.htm

References

Bock H-H. and Diday E. (eds.) (2000).Analysis of Symbolic Data. Exploratory methods for extracting statistical information fromcomplex data. Springer, Germany.

Examples

data(abalone)
res <- sym.pca(abalone, 'centers')
plot(res, choix = "ind")
plot(res, choix = "var")

Description

a data.frame

Usage

## S3 method for class 'symbolic_histogram'
as.data.frame(x, ...)

Arguments

Description

convertir a data.frame

Usage

## S3 method for class 'symbolic_interval'
as.data.frame(x, ...)

Arguments

Description

Extract values

Usage

## S3 method for class 'symbolic_modal'
as.data.frame(x, ...)

Arguments

Description

convertir a data.frame

Usage

## S3 method for class 'symbolic_set'
as.data.frame(x, ...)

Arguments

Description

Burt Matrix

Usage

calc.burt.sym(sym.data, pos.var)

Arguments

Description

quantiles.RSDA

Usage

calculate.quantils.RSDA(histogram.RSDA, num.quantils)

Arguments

Value

Quantiles of a Histogram

Description

Cardiological interval data example.

Usage

data(Cardiological)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 11 rows and 3 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(Cardiological)
res.cm <- sym.lm(formula = Pulse~Syst+Diast, sym.data = Cardiological, method = 'cm')
pred.cm <- sym.predict(res.cm, Cardiological)
RMSE.L(Cardiological$Pulse, pred.cm$Fitted)
RMSE.U(Cardiological$Pulse,pred.cm$Fitted)
R2.L(Cardiological$Pulse,pred.cm$Fitted)
R2.U(Cardiological$Pulse,pred.cm$Fitted)
deter.coefficient(Cardiological$Pulse,pred.cm$Fitted)

Description

Cardiological interval data example.

Usage

data(Cardiological)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 44 rows and 5 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Description

Compute centers of the interval

Usage

centers.interval(sym.data)

Arguments

Value

Centers of teh intervals.

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984).Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Generate a symbolic data table from a classic data table.

Usage

classic.to.sym(
  x = NULL,
  concept = NULL,
  variables = tidyselect::everything(),
  default.numeric = sym.interval,
  default.categorical = sym.modal,
  ...
)

Arguments

Value

a [tibble][tibble::tibble-package]

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Description

This function compute the symbolic correlation

Usage

cor(x, ...)

## Default S3 method:
cor(
  x,
  y = NULL,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  ...
)

## S3 method for class 'symbolic_interval'
cor(x, y, method = c("centers", "billard"), ...)

## S3 method for class 'symbolic_tbl'
cor(x, ...)

Arguments

Value

Return a real number in [-1,1].

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

This function compute the symbolic covariance.

Usage

cov(x, ...)

## Default S3 method:
cov(
  x,
  y = NULL,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  ...
)

## S3 method for class 'symbolic_interval'
cov(x, y, method = c("centers", "billard"), na.rm = FALSE, ...)

## S3 method for class 'symbolic_tbl'
cov(x, ...)

Arguments

Value

Return a real number.

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

The determination coefficient represents a goodness-of-fit measure commonly used in regression analysis to capture the adjustment quality of a model.

Usage

deter.coefficient(ref, pred)

Arguments

Value

Return the determination cosfficient.

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Examples

data(int_prost_test)
data(int_prost_train)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(res.cm, int_prost_test)
deter.coefficient(int_prost_test$lpsa, pred.cm$Fitted)

Description

Compute a distance vector

Usage

dist.vect(vector1, vector2)

Arguments

Value

Eclidean distance between the two vectors.

Author(s)

Jorge Arce

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D. Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Compute the distance vector matrix.

Usage

dist.vect.matrix(vector, Matrix)

Arguments

Value

The distance.

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Correspondence Analysis for Symbolic MultiValued Variables example.

Usage

data(ex_cfa1)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 4 rows and 4 columns.

References

Rodriguez, O. (2011). Correspondence Analysis for Symbolic MultiValued Variables. Workshop in Symbolic Data Analysis Namur, Belgium

Description

Correspondence Analysis for Symbolic MultiValued Variables example.

Usage

data(ex_cfa2)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 6 rows and 5 columns.

References

Rodriguez, O. (2011). Correspondence Analysis for Symbolic MultiValued Variables. Workshop in Symbolic Data Analysis Namur, Belgium

Description

example for the sym.mcfa function.

example for the sym.mcfa function.

Usage

data(ex_mcfa1)

ex_mcfa1

Format

An object of class data.frame with 130 rows and 5 columns.

An object of class data.frame with 130 rows and 5 columns.

Examples

data("ex_mcfa1")
sym.table <- classic.to.sym(ex_mcfa1,
                            concept = suspect,
                            hair = sym.set(hair),
                            eyes = sym.set(eyes),
                            region = sym.set(region))

res <- sym.mcfa(sym.table, c(1,2))
mcfa.scatterplot(res[,1], res[,2], sym.data = sym.table, pos.var = c(1,2))

data("ex_mcfa1")
sym.table <- classic.to.sym(
  x = ex_mcfa1,
  concept = "suspect",
  variables = c(hair, eyes, region),
  hair = sym.set(hair),
  eyes = sym.set(eyes),
  region = sym.set(region)
)
sym.table

Description

example for the sym.mcfa function.

Usage

data(ex_mcfa2)

Format

An object of class data.frame with 130 rows and 7 columns.

Examples

data("ex_mcfa2")

ex <- classic.to.sym(ex_mcfa2,
                    concept = employee_id,
                     variables = c(employee_id, salary, region, evaluation, years_worked),
                     salary = sym.set(salary),
                     region = sym.set(region),
                     evaluation = sym.set(evaluation),
                     years_worked = sym.set(years_worked))

res <- sym.mcfa(ex, c(1,2,3,4))
mcfa.scatterplot(res[,1], res[,2], sym.data = ex, pos.var = c(1,2,3,4))

Description

This is a small data example to generate symbolic objets.

Usage

data(ex1_db2so)

Format

An object of class data.frame with 19 rows and 5 columns.

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Examples

data(ex1_db2so)
ex1 <- ex1_db2so
result <- classic.to.sym(
  x = ex1_db2so,
  concept = c(state, sex),
  variables = c(county, group, age),
  county = mean(county),
  age_hist = sym.histogram(age, breaks = pretty(ex1_db2so$age, 5))
)
result

Description

This a symbolic data table with variables of continuos, interval, histogram and set types.

Usage

data(example1)

Format

The labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories). In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.
The format is the *.csv file is:
$C F1 $I F2 F2 $M F3 M1 M2 M3 $S F4 e a 2 3 g b 1 4 i k c d
Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $S 12 0 1 0 0 0 1 0 0 0 0 1 1
Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $S 12 0 0 1 0 0 1 1 0 0 0 1 0
Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $S 12 0 1 0 1 0 0 0 1 0 0 1 0
Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
The internal format is:
$N
[1] 5
$M
[1] 4
$sym.obj.names
[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5'
$sym.var.names
[1] 'F1' 'F2' 'F3' 'F4'
$sym.var.types [1] '$C' '$I' '$H' '$S'
$sym.var.length
[1] 1 2 3 4
$sym.var.starts
[1] 2 4 8 13
$meta

$C F1 $I F2 F2 $M F3 M1 M2 M3 $S F4 e a 2 3 g b 1 4 i k c d Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $data
F1 F2 F2.1 M1 M2 M3 e a 2 3 g b 1 4 i k c d Case1 2.8 1 2 0.1 0.7 0.2 1 0 0 0 1 0 0 0 1 1 0 0 Case2 1.4 3 9 0.6 0.3 0.1 0 1 0 0 0 1 0 0 0 0 1 1 Case3 3.2 -1 4 0.2 0.2 0.6 0 0 1 0 0 1 1 0 0 0 1 0 Case4 -2.1 0 2 0.9 0.0 0.1 0 1 0 1 0 0 0 1 0 0 1 0 Case5 -3.0 -4 -2 0.6 0.0 0.4 1 0 0 0 1 0 0 0 1 1 0 0

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Examples

data(example1)
example1

Description

This a symbolic data table with variables of continuos, interval, histogram and set types.

Usage

data(example2)

Format

$C F1 $I F2 F2 $M F3 M1 M2 M3 $C F4 $S F5 e a 2 3 g b 1 4 i k c d
Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $C 6.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1
Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0
Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 1 0 1 0 0 0 1 0 0 1 0
Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 1 0 0 0 1 0 0 0 1 1 0 0

Examples

data(example2)
example2

Description

This a symbolic data table with variables of continuos, interval, histogram and set types.

Usage

data(example3)

Format

$C F1 $I F2 F2 $M F3 M1 M2 M3 $C F4 $S F5 e a 2 3 g b 1 4 i k c d $I F6 F6 $I F7 F7 Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $C 6.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I 0.00 90.00 $I 9 24 Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 $I -90.00 98.00 $I -9 9 Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 $I 65.00 90.00 $I 65 70 Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 $I 45.00 89.00 $I 25 67 Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I 20.00 40.00 $I 9 40 Case6 $C 0.1 $I 10 21 $M 3 0.0 0.7 0.3 $C -1.0 $S 12 1 0 0 0 0 0 1 0 1 0 0 0 $I 5.00 8.00 $I 5 8 Case7 $C 9.0 $I 4 21 $M 3 0.2 0.2 0.6 $C 0.5 $S 12 1 1 1 0 0 0 0 0 0 0 0 0 $I 3.14 6.76 $I 4 6

Examples

data(example3)
example3

Description

data(example4) example4

Usage

data(example4)

Format

$C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $C 6 $S F4 e a 2 3 g b 1 4 i k c d $I 0 90 Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I -90.00 98.00 Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 $I 65.00 90.00 Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 $I 45.00 89.00 Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 $I 90.00 990.00 Case6 $C 0.1 $I 10 21 $M 3 0.0 0.7 0.3 $C -1.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I 5.00 8.00 Case7 $C 9.0 $I 4 21 $M 3 0.2 0.2 0.6 $C 0.5 $S 12 1 1 0 0 0 0 1 0 0 0 0 1 $I 3.14 6.76

Examples

data(example4)
example4

Description

This a symbolic data matrix wint continuos, interval, histograma a set data types.

Usage

data(example5)

Format

$H F0 M01 M02 $C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4
Case1 $H 2 0.1 0.9 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i
Case2 $H 2 0.7 0.3 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d
Case3 $H 2 0.0 1.0 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c
Case4 $H 2 0.2 0.8 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a
Case5 $H 2 0.6 0.4 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k

Examples

data(example5)
example5

Description

This a symbolic data matrix wint continuos, interval, histograma a set data types.

Usage

data(example6)

Format

$C F1 $M F2 M1 M2 M3 M4 M5 $I F3 F3 $M F4 M1 M2 M3 $C F5 $S F4 e a 2 3 g b 1 4 i k c d Case1 $C 2.8 $M 5 0.1 0.1 0.1 0.1 0.6 $I 1 2 $M 3 0.1 0.7 0.2 $C 6.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 Case2 $C 1.4 $M 5 0.1 0.1 0.1 0.1 0.6 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 Case3 $C 3.2 $M 5 0.1 0.1 0.1 0.1 0.6 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 Case4 $C -2.1 $M 5 0.1 0.1 0.1 0.1 0.6 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 Case5 $C -3.0 $M 5 0.1 0.1 0.1 0.1 0.6 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 1 0 0 0 1 0 0 0 1 1 0 0

Examples

data(example6)
example6

Description

This a symbolic data matrix wint continuos, interval, histograma a set data types.

Usage

data(example6)

Format

$C F1 $H F2 M1 M2 M3 M4 M5 $I F3 F3 $H F4 M1 M2 M3 $C F5
Case1 $C 2.8 $H 5 0.1 0.2 0.3 0.4 0.0 $I 1 2 $H 3 0.1 0.7 0.2 $C 6.0
Case2 $C 1.4 $H 5 0.2 0.1 0.5 0.1 0.2 $I 3 9 $H 3 0.6 0.3 0.1 $C 8.0
Case3 $C 3.2 $H 5 0.1 0.1 0.2 0.1 0.5 $I -1 4 $H 3 0.2 0.2 0.6 $C -7.0
Case4 $C -2.1 $H 5 0.4 0.1 0.1 0.1 0.3 $I 0 2 $H 3 0.9 0.0 0.1 $C 0.0
Case5 $C -3.0 $H 5 0.6 0.1 0.1 0.1 0.1 $I -4 -2 $H 3 0.6 0.0 0.4 $C -9.5

Examples

data(example7)
example7

Description

Symbolic data matrix with all the variables of interval type.

Usage

data('facedata')

Format

$I;AD;AD;$I;BC;BC;.........

HUS1;$I;168.86;172.84;$I;58.55;63.39;.........
HUS2;$I;169.85;175.03;$I;60.21;64.38;.........
HUS3;$I;168.76;175.15;$I;61.4;63.51;.........
INC1;$I;155.26;160.45;$I;53.15;60.21;.........
INC2;$I;156.26;161.31;$I;51.09;60.07;.........
INC3;$I;154.47;160.31;$I;55.08;59.03;.........
ISA1;$I;164;168;$I;55.01;60.03;.........
ISA2;$I;163;170;$I;54.04;59;.........
ISA3;$I;164.01;169.01;$I;55;59.01;.........
JPL1;$I;167.11;171.19;$I;61.03;65.01;.........
JPL2;$I;169.14;173.18;$I;60.07;65.07;.........
JPL3;$I;169.03;170.11;$I;59.01;65.01;.........
KHA1;$I;149.34;155.54;$I;54.15;59.14;.........
KHA2;$I;149.34;155.32;$I;52.04;58.22;.........
KHA3;$I;150.33;157.26;$I;52.09;60.21;.........
LOT1;$I;152.64;157.62;$I;51.35;56.22;.........
LOT2;$I;154.64;157.62;$I;52.24;56.32;.........
LOT3;$I;154.83;157.81;$I;50.36;55.23;.........
PHI1;$I;163.08;167.07;$I;66.03;68.07;.........
PHI2;$I;164;168.03;$I;65.03;68.12;.........
PHI3;$I;161.01;167;$I;64.07;69.01;.........
ROM1;$I;167.15;171.24;$I;64.07;68.07;.........
ROM2;$I;168.15;172.14;$I;63.13;68.07;.........
ROM3;$I;167.11;171.19;$I;63.13;68.03;.........

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

## Not run: 
data(facedata)
res.vertex.ps <- sym.interval.pc(facedata,'vertex',150,FALSE,FALSE,TRUE)
class(res.vertex.ps$sym.prin.curve) <- c('sym.data.table')
sym.scatterplot(res.vertex.ps$sym.prin.curve[,1], res.vertex.ps$sym.prin.curve[,2],
                labels=TRUE,col='red',main='PSC Face Data')
                
## End(Not run)

Description

Symbolic modal conversion functions to and from Character

Usage

## S3 method for class 'symbolic_histogram'
format(x, ...)

Arguments

Description

Symbolic interval conversion functions to and from Character

Usage

## S3 method for class 'symbolic_interval'
format(x, ...)

Arguments

Description

Symbolic modal conversion functions to and from Character

Usage

## S3 method for class 'symbolic_modal'
format(x, ...)

Arguments

Description

Symbolic set conversion functions to and from Character

Usage

## S3 method for class 'symbolic_set'
format(x, ...)

Arguments

Description

Extract categories

Usage

get_cats(x, ...)

Arguments

Description

Extract prop

Usage

get_props(x, ...)

Arguments

Description

Calculate the interval projection onto the principal components

Usage

get.limits.PCA(sym.data, matrix.stan, min.stan, max.stan, svd, nn, mm)

Arguments

Value

Concept Projections onto the principal components and correlation circle

Description

Calculate the interval projection onto the principal components

Usage

get.limits.PCA.indivduals(
  sym.data,
  matrix.stan,
  min.stan,
  max.stan,
  svd,
  nn,
  mm
)

Arguments

Value

Concept Projections onto the principal components

Description

Symbolic Histogram matrix.

Usage

data('hardwoodBrito')

Format

An object of class symbolic_tbl (inherits from symbolic_tbl, symbolic_tbl, symbolic_tbl, tbl_df, tbl, data.frame) with 5 rows and 4 columns.

References

Brito P. and Dias S. (2022). Analysis of Distributional Data. CRC Press, United States of America.

Examples

## Not run: 
data(hardwoodBrito)
hardwoodBrito

## End(Not run)

Description

HistRSDAToEcdf

Usage

HistRSDAToEcdf(h)

Arguments

Value

Transformation in Ecdf object

Author(s)

Jorge Arce Garro

Examples

## Not run: 
 data("hardwoodBrito")
 Hardwood.histogram<-hardwoodBrito
 Hardwood.cols<-colnames(Hardwood.histogram)
 Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
 pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
 Hardwood.quantiles.PCA.2<-quantiles.RSDA.KS(pca.hist$sym.hist.matrix.PCA,100)
 h<-Hardwood.quantiles.PCA.2[[1]][[1]]
 HistRSDAToEcdf(h)

## End(Not run)

Description

Linear regression model interval-valued data example.

Usage

data(int_prost_test)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 30 rows and 9 columns.

References

HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.

Description

Linear regression model interval-valued data example.

Usage

data(int_prost_train)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 67 rows and 9 columns.

References

HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.

Description

calcula centros

Usage

interval.centers(x)

Arguments

Description

Histogram plot for an interval variable

Usage

interval.histogram.plot(x, n.bins, ...)

Arguments

Value

A list with componets : frequency and histogram

Examples

data(oils)
res <- interval.histogram.plot(x = oils[, 3], n.bins = 3)
res

Description

Calculate the large of each interval

Usage

interval.large(x)

Arguments

Value

A matrix with the large of each interval.

Examples

## Not run: 
data(oils)
interval.large(oils)

## End(Not run)

Description

Calculate the large of each interval

Usage

interval.length(x)

Arguments

Value

A matrix with the length of each interval.

Examples

## Not run: 
data(oils)
interval.length(oils)

## End(Not run)

Description

calcula maximos

Usage

interval.max(x)

Arguments

Description

calcula minimos

Usage

interval.min(x)

Arguments

Description

calcula rangos

Usage

interval.ranges(x)

Arguments

Description

Symbolic histogram

Usage

is.sym.histogram(x)

Arguments

Value

returns TRUE if its argument's value is a symbolic_histogram and FALSE otherwise.

Examples

x <- sym.histogram(iris$Sepal.Length)
is.sym.histogram(x)

Description

Symbolic interval

Usage

is.sym.interval(x)

Arguments

Value

returns TRUE if its argument's value is a symbolic_vector and FALSE otherwise.

Examples

x <- sym.interval(1:10)
is.sym.interval(x)
is.sym.interval("d")

Description

Symbolic modal

Usage

is.sym.modal(x)

Arguments

Value

returns TRUE if its argument's value is a symbolic_modal and FALSE otherwise.

Examples

x <- sym.modal(factor(c("a", "b", "b", "l")))
is.sym.modal(x)

Description

Symbolic set

Usage

is.sym.set(x)

Arguments

Value

returns TRUE if its argument's value is a symbolic_set and FALSE otherwise.

Examples

x <- sym.set(factor(c("a", "b", "b", "l")))
is.sym.set(x)

Description

Symbolic data matrix with all the variables of interval type.

Usage

data(lynne1)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 10 rows and 4 columns.

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(lynne1)
lynne1

Description

Plot Interval Scatterplot

Usage

mcfa.scatterplot(x, y, sym.data, pos.var)

Arguments

Examples

data("ex_mcfa1")
sym.table <- classic.to.sym(ex_mcfa1,
  concept = suspect,
  hair = sym.set(hair),
  eyes = sym.set(eyes),
  region = sym.set(region)
)

res <- sym.mcfa(sym.table, c(1, 2))
mcfa.scatterplot(res[, 2], res[, 3], sym.data = sym.table, pos.var = c(1, 2))

Description

This function compute the symbolic mean for intervals

Usage

## S3 method for class 'symbolic_interval'
mean(x, method = c("centers", "interval"), trim = 0, na.rm = F, ...)

## S3 method for class 'symbolic_tbl'
mean(x, ...)

Arguments

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

This function compute the median for symbolic intervals.

Usage

## S3 method for class 'symbolic_interval'
median(x, na.rm = FALSE, method = c("centers", "interval"), ...)

## S3 method for class 'symbolic_tbl'
median(x, ...)

Arguments

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

Summary method to CM and CRM regression model

Usage

method_summary(ref, pred)

Arguments

Description

Maxima and Minima

Usage

## S3 method for class 'symbolic_interval'
min(x, ...)

## S3 method for class 'symbolic_interval'
max(x, ...)

## S3 method for class 'symbolic_interval'
x$name = c("min", "max", "mean", "median")

Arguments

Value

a new symbolic interval with the minimum of the minima and the minimum of the maxima

Description

Compute neighbors vertex

Usage

neighbors.vertex(vertex, Matrix, num.neig)

Arguments

Author(s)

Jorge Arce

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Compute the norm of a vector.

Usage

norm.vect(vector1)

Arguments

Value

The L2 norm of the vector.

Author(s)

Jorge Arce

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Symbolic data matrix with all the variables of interval type.

Usage

data(oils)

Format

$I GRA GRA $I FRE FRE $I IOD IOD $I SAP SAP
L $I 0.930 0.935 $I -27 -18 $I 170 204 $I 118 196
P $I 0.930 0.937 $I -5 -4 $I 192 208 $I 188 197
Co $I 0.916 0.918 $I -6 -1 $I 99 113 $I 189 198
S $I 0.920 0.926 $I -6 -4 $I 104 116 $I 187 193
Ca $I 0.916 0.917 $I -25 -15 $I 80 82 $I 189 193
O $I 0.914 0.919 $I 0 6 $I 79 90 $I 187 196
B $I 0.860 0.870 $I 30 38 $I 40 48 $I 190 199
H $I 0.858 0.864 $I 22 32 $I 53 77 $I 190 202

References

Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.

Examples

data(oils)
oils

Description

Calculate the distance

Usage

pca.supplementary.vertex.fun.j.new(
  x,
  N,
  M,
  sym.var.names,
  sym.data.vertex.matrix,
  tot.individuals
)

Arguments

Value

Distance

Description

Percentil.Arrow.plot

Usage

Percentil.Arrow.plot(
  quantiles.sym,
  concept.names,
  var.names,
  Title,
  axes.x.label,
  axes.y.label,
  label.name
)

Arguments

Value

Arrow Plot

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "First Principal Component (84.83%)"
axes.y.label<- "Second Principal Component (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
quantile.ACER.plot<-Percentil.Arrow.plot(Hardwood.quantiles.PCA,
                                        concept.names,
                                        var.names,
                                        Title,
                                        axes.x.label,
                                        axes.y.label,
                                        label.name
                                        )
quantile.ACER.plot

## End(Not run)

Description

Plot UMAP for symbolic data tables

Usage

## S3 method for class 'sym_umap'
plot(x, ...)

Arguments

Description

Function for plotting a symbolic object

Usage

## S3 method for class 'symbolic_tbl'
plot(
  x,
  col = NA,
  matrix.form = NA,
  border = FALSE,
  size = 1,
  title = TRUE,
  show.type = FALSE,
  font.size = 1,
  reduce = FALSE,
  hist.angle.x = 60,
  ...
)

Arguments

Value

A plot of the symbolic data table.

Author(s)

Andres Navarro

Examples

## Not run: 
data(oils)
plot(oils)
plot(oils, border = T, size = 1.3)

## End(Not run)

Description

quantiles.RSDA

Usage

quantiles.RSDA(histogram.matrix, num.quantiles)

Arguments

Value

Quantiles of a Histogram Matrix

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)

## End(Not run)

Description

quantiles.RSDA.KS

Usage

quantiles.RSDA.KS(histogram.matrix, num.quantiles)

Arguments

Value

Quantiles of a Histogram Matrix

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
quantiles.RSDA.KS<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,100)

## End(Not run)

Description

Compute the lower boundary correlation coefficient for two interval variables.

Usage

R2.L(ref, pred)

Arguments

Value

The lower boundary correlation coefficient.

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Examples

data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(res.cm, int_prost_test)
R2.L(int_prost_test$lpsa, pred.cm$Fitted)

Description

Compute the upper boundary correlation coefficient for two interval variables.

Usage

R2.U(ref, pred)

Arguments

Value

The upper boundary correlation coefficient.

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Examples

data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(res.cm, int_prost_test)
R2.U(int_prost_test$lpsa, pred.cm$Fitted)

Description

It reads a symbolic data table from a CSV file.

Usage

read.sym.table(file, header = TRUE, sep, dec, row.names = NULL)

Arguments

Details

The labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories) . In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.

The format is the CSV file should be like:

$C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4

Case1 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i

Case2 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d

Case3 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c

Case4 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a

Case5 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k

The internal format is:
$N
[1] 5
$M
[1] 4
$sym.obj.names
[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5'
$sym.var.names
[1] 'F1' 'F2' 'F3' 'F4'
$sym.var.types
[1] '$C' '$I' '$H' '$S'
$sym.var.length
[1] 1 2 3 4
$sym.var.starts
[1] 2 4 8 13
$meta
$C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4
Case1 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i
Case2 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d
Case3 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c
Case4 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a
Case5 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k
$data
F1 F2 F2.1 M1 M2 M3 E1 E2 E3 E4
Case1 2.8 1 2 0.1 0.7 0.2 e g k i
Case2 1.4 3 9 0.6 0.3 0.1 a b c d
Case3 3.2 -1 4 0.2 0.2 0.6 2 1 b c
Case4 -2.1 0 2 0.9 0.0 0.1 3 4 c a
Case5 -3.0 -4 -2 0.6 0.0 0.4 e i g k

Value

Return a symbolic data table structure.

Author(s)

Oldemar Rodriguez Rojas

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

See Also

display.sym.table

Examples

## Not run: 
data(example1)
write.sym.table(example1,
  file = "temp4.csv", sep = "|", dec = ".", row.names = TRUE,
  col.names = TRUE
)
ex1 <- read.sym.table("temp4.csv", header = TRUE, sep = "|", dec = ".", row.names = 1)

## End(Not run)

Description

Compute the lower boundary root-mean-square error.

Usage

RMSE.L(ref, pred)

Arguments

Value

The lower boundary root-mean-square error.

Author(s)

Oldemar Rodriguez Rojas.

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Description

Compute the upper boundary root-mean-square error.

Usage

RMSE.U(ref, pred)

Arguments

Value

The upper boundary root-mean-square error.

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Description

This work is framed inside the Symbolic Data Analysis (SDA). The objective of this work is to implement in R to the symbolic case certain techniques of the automatic classification, as well as some lineal models. These implementations will always be made following two fundamental principles in Symbolic Data Analysis like they are: Classic Data Analysis should always be a case particular case of the Symbolic Data Analysis and both, the exit as the input in an Symbolic Data Analysis should be symbolic. We implement for variables of type interval the mean, the median, the mean of the extreme values, the standard deviation, the deviation quartil, the dispersion boxes and the correlation also three new methods are also presented to carry out the lineal regression for variables of type interval. We also implement in this R package the method of Principal Components Analysis in two senses: First, we propose three ways to project the interval variables in the circle of correlations in such way that is reflected the variation or the inexactness of the variables. Second, we propose an algorithm to make the Principal Components Analysis for variables of type histogram. We implement a method for multidimensional scaling of interval data, denominated INTERSCAL.

Details

Most of the function of the package stars from a symbolic data table that can be store in a CSV file withe follwing forma: In the first row the labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories) . In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.

Author(s)

Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas <[email protected]>

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Billard L., Douzal-Chouakria A. and Diday E. (2011) Symbolic Principal Components For Interval-Valued Observations, Statistical Analysis and Data Mining. 4 (2), 229-246. Wiley.

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Carvalho F., Souza R.,Chavent M., and Lechevallier Y. (2006) Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters Volume 27, Issue 3, February 2006, Pages 167-179

Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.

Diday, E., Rodriguez O. and Winberg S. (2000). Generalization of the Principal Components Analysis to Histogram Data, 4th European Conference on Principles and Practice of Knowledge Discovery in Data Bases, September 12-16, 2000, Lyon, France.

Chouakria A. (1998) Extension des methodes d'analysis factorialle a des donnees de type intervalle, Ph.D. Thesis, Paris IX Dauphine University.

Makosso-Kallyth S. and Diday E. (2012). Adaptation of interval PCA to symbolic histogram variables, Advances in Data Analysis and Classification July, Volume 6, Issue 2, pp 147-159. Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

Compute the symbolic standard desviation.

Usage

sd(x, ...)

## Default S3 method:
sd(x, na.rm = FALSE, ...)

## S3 method for class 'symbolic_interval'
sd(x, method = c("centers", "interval", "billard"), na.rm = FALSE, ...)

## S3 method for class 'symbolic_tbl'
sd(x, ...)

Arguments

Value

return a real number.

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

To convert SDS SODAS files to RSDA files.

Usage

SDS.to.RSDA(file.path, labels = FALSE)

Arguments

Value

A RSDA symbolic data file.

Author(s)

Olger Calderon and Roberto Zuniga.

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

See Also

SODAS.to.RSDA

Examples

## Not run: 
# We can read the file directly from the SODAS SDA file as follows:
# We can save the file in CSV to RSDA format as follows:
setwd('C:/Program Files (x86)/DECISIA/SODAS version 2.0/bases/')
result <- SDS.to.RSDA(file.path='hani3101.sds')
# We can save the file in CSV to RSDA format as follows:
write.sym.table(result, file='hani3101.csv', sep=';',dec='.', row.names=TRUE,

## End(Not run)

Description

To convert XML SODAS files to RSDA files.

Usage

SODAS.to.RSDA(XMLPath, labels = T)

Arguments

Value

A RSDA symbolic data file.

Author(s)

Olger Calderon and Roberto Zuniga.

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

See Also

SDS.to.RSDA

Examples

## Not run: 
# We can read the file directly from the SODAS XML file as follows:
# abalone<-SODAS.to.RSDA('C:/Program Files (x86)/DECISIA/SODAS version 2.0/bases/abalone.xml)
# We can save the file in CSV to RSDA format as follows:
# write.sym.table(sodas.ex1, file='abalone.csv', sep=';',dec='.', row.names=TRUE,
#               col.names=TRUE)
# We read the file from the CSV file,
# this is not necessary if the file is read directly from
# XML using SODAS.to.RSDA as in the first statement in this example.
data(abalone)
res <- sym.interval.pca(abalone, "centers")
sym.scatterplot(sym.var(res$Sym.Components, 1), sym.var(res$Sym.Components, 2),
  labels = TRUE, col = "red", main = "PCA Oils Data"
)
sym.scatterplot3d(sym.var(res$Sym.Components, 1), sym.var(res$Sym.Components, 2),
  sym.var(res$Sym.Components, 3),
  color = "blue", main = "PCA Oils Data"
)
sym.scatterplot.ggplot(sym.var(res$Sym.Components, 1), sym.var(res$Sym.Components, 2),
  labels = TRUE
)
sym.circle.plot(res$Sym.Prin.Correlations)

## End(Not run)

Description

Standardized Intervals

Usage

stand.data(sym.data, data.mean, data.stan, nn, mm)

Arguments

Value

Standardized intervals

Description

sym.all.quantiles.mesh3D.plot

Usage

sym.all.quantiles.mesh3D.plot(
  quantiles.sym,
  concept.names,
  var.names,
  Title,
  axes.x.label,
  axes.y.label,
  label.name
)

Arguments

Value

3D Mesh Plot

Author(s)

Jorge Arce Garro

Examples

## Not run: 
 data("hardwoodBrito")
 Hardwood.histogram<-hardwoodBrito
 Hardwood.cols<-colnames(Hardwood.histogram)
 Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
 pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
 Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
 label.name<-"Hard Wood"
 Title<-"First Principal Plane"
 axes.x.label<- "First Principal Component (84.83%)"
 axes.y.label<- "Second Principal Component (9.70%)"
 concept.names<-c("ACER")
 var.names<-c("PC.1","PC.2")
 concept.names<-row.names(Hardwood.quantiles.PCA)
 sym.all.quantiles.mesh3D.plot(Hardwood.quantiles.PCA,
                           concept.names,
                           var.names,
                           Title,
                           axes.x.label,
                           axes.y.label,
                           label.name)

## End(Not run)

Description

sym.all.quantiles.plot

Usage

sym.all.quantiles.plot(
  quantiles.sym,
  concept.names,
  var.names,
  Title,
  axes.x.label,
  axes.y.label,
  label.name
)

Arguments

Value

3D Scatter Plot

Author(s)

Jorge Arce Garro

Examples

## Not run: 
 data("hardwoodBrito")
 Hardwood.histogram<-hardwoodBrito
 Hardwood.cols<-colnames(Hardwood.histogram)
 Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
 pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
 Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
 label.name<-"Hard Wood"
 Title<-"First Principal Plane"
 axes.x.label<- "First Principal Component (84.83%)"
 axes.y.label<- "Second Principal Component (9.70%)"
 concept.names<-c("ACER")
 var.names<-c("PC.1","PC.2")

 concept.names<-row.names(Hardwood.quantiles.PCA)
 sym.all.quantiles.plot(Hardwood.quantiles.PCA,
                           concept.names,
                           var.names,
                           Title,
                           axes.x.label,
                           axes.y.label,
                           label.name)

## End(Not run)

Description

Plot the symbolic circle of correlations.

Usage

sym.circle.plot(prin.corre)

Arguments

Value

Plot the symbolic circle

Author(s)

Oldemar Rodriguez Rojas

References

Rodriguez O. (2012). The Duality Problem in Interval Principal Components Analysis. The 3rd Workshop in Symbolic Data Analysis, Madrid.

Examples

data(oils)
res <- sym.pca(oils, "centers")
sym.circle.plot(res$Sym.Prin.Correlations)

Description

This function computes and returns the distance matrix by using the specified distance measure to compute distance between symbolic interval variables.

Usage

sym.dist.interval(
  sym.data,
  gamma = 0.5,
  method = "Minkowski",
  normalize = TRUE,
  SpanNormalize = FALSE,
  q = 1,
  euclidea = TRUE,
  pond = rep(1, length(variables))
)

Arguments

Value

An object of class 'dist'

Description

Generalized Boosted Symbolic Regression

Usage

sym.gbm(
  formula,
  sym.data,
  method = c("cm", "crm"),
  distribution = "gaussian",
  interaction.depth = 1,
  n.trees = 500,
  shrinkage = 0.1
)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Execute Lasso, Ridge and and Elastic Net Linear regression model to interval variables.

Usage

sym.glm(sym.data, response = 1, method = c('cm', 'crm'),
alpha = 1, nfolds = 10, grouped = TRUE)

Arguments

Value

An object of class 'cv.glmnet' is returned, which is a list with the ingredients of the cross-validation fit.

Author(s)

Oldemar Rodriguez Rojas

References

Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.

See Also

sym.lm

Description

Create an symbolic_histogram type object

Usage

sym.histogram(x = double(), breaks = NA_real_)

Arguments

Value

a symbolic histogram

Examples

sym.histogram(iris$Sepal.Length)

Description

sym.histogram.pca

Usage

sym.histogram.pca(sym.hist.matrix, BIN.Matrix, method = NULL)

Arguments

Value

Histogram PCA

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
weighted.center<-weighted.center.Hist.RSDA(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
pca.hist

## End(Not run)

Description

Create an symbolic_interval type object

Usage

sym.interval(x = numeric(), .min = min, .max = max)

Arguments

Value

a symbolic interval

Examples

sym.interval(c(1, 2, 4, 5))
sym.interval(1:10)

Description

Compute a symbolic interval principal components curves

Usage

sym.interval.pc(sym.data, method = c('vertex', 'centers'), maxit, plot, scale, center)

Arguments

Value

prin.curve: This a symbolic data table with the interval principal components. As this is a symbolic data table we can apply over this table any other symbolic data analysis method (symbolic propagation).

cor.ps: This is the interval correlations between the original interval variables and the interval principal components, it can be use to plot the symbolic circle of correlations.

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pca

Examples

## Not run: 
data(oils)
res.vertex.ps <- sym.interval.pc(oils, "vertex", 150, FALSE, FALSE, TRUE)
class(res.vertex.ps$sym.prin.curve) <- c("sym.data.table")
sym.scatterplot(res.vertex.ps$sym.prin.curve[, 1], res.vertex.ps$sym.prin.curve[, 2],
  labels = TRUE, col = "red", main = "PSC Oils Data"
)

data(facedata)
res.vertex.ps <- sym.interval.pc(facedata, "vertex", 150, FALSE, FALSE, TRUE)
class(res.vertex.ps$sym.prin.curve) <- c("sym.data.table")
sym.scatterplot(res.vertex.ps$sym.prin.curve[, 1], res.vertex.ps$sym.prin.curve[, 2],
  labels = TRUE, col = "red", main = "PSC Face Data"
)

## End(Not run)

Description

Symbolic interval principal curves limits.

Usage

sym.interval.pc.limits(sym.data, prin.curve, num.vertex, lambda, var.ord)

Arguments

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

This is a function is to carry out a k-means overs a interval symbolic data matrix.

Usage

sym.kmeans(sym.data, k = 3, iter.max = 10, nstart = 1,
algorithm = c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen'))

Arguments

Value

This function return the following information:

K-means clustering with 3 clusters of sizes 2, 2, 4

Cluster means:

GRA FRE IOD SAP

1 0.93300 -13.500 193.500 174.75

2 0.86300 30.500 54.500 195.25

3 0.91825 -6.375 95.375 191.50

Clustering vector:

L P Co S Ca O B H

1 1 3 3 3 3 2 2

Within cluster sum of squares by cluster:

[1] 876.625 246.125 941.875

(between_SS / total_SS = 92.0

Available components:

[1] 'cluster' 'centers' 'totss' 'withinss' 'tot.withinss' 'betweenss'

[7] 'size'

Author(s)

Oldemar Rodriguez Rojas

References

Carvalho F., Souza R.,Chavent M., and Lechevallier Y. (2006) Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters Volume 27, Issue 3, February 2006, Pages 167-179

See Also

sym.hclust

Examples

data(oils)
sk <- sym.kmeans(oils, k = 3)
sk$cluster

Description

Symbolic k-Nearest Neighbor Regression

Usage

sym.knn(
  formula,
  sym.data,
  method = c("cm", "crm"),
  scale = TRUE,
  kmax = 20,
  kernel = "triangular"
)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

To execute the Center Method (CR) and Center and Range Method (CRM) to Linear regression.

Usage

sym.lm(formula, sym.data, method = c('cm', 'crm'))

Arguments

Details

Models for lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

Value

sym.lm returns an object of class 'lm' or for multiple responses of class c('mlm', 'lm')

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

Examples

data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
res.cm

Description

This function executes a Multiple Correspondence Factor Analysis for variables of set type.

Usage

sym.mcfa(sym.data, pos.var)

Arguments

Author(s)

Jorge Arce

References

Arce J. and Rodriguez, O. (2018). Multiple Correspondence Analysis for Symbolic Multi–Valued Variables. On the Symbolic Data Analysis Workshop SDA 2018.

Benzecri, J.P. (1973). L' Analyse des Données. Tomo 2: L'Analyse des Correspondances. Dunod, Paris.

Castillo, W. and Rodriguez O. (1997). Algoritmo e implementacion del analisis factorial de correspondencias. Revista de Matematicas: Teoria y Aplicaciones, 24-31.

Takagi I. and Yadosiha H. (2011). Correspondence Analysis for symbolic contingency tables base on interval algebra. Elsevier Procedia Computer Science, 6, 352-357.

Rodriguez, O. (2007). Correspondence Analysis for Symbolic Multi–Valued Variables. CARME 2007 (Rotterdam, The Netherlands), http://www.carme-n.org/carme2007.

Examples

data("ex_mcfa1")
sym.table <- classic.to.sym(ex_mcfa1,
  concept = suspect,
  hair = sym.set(hair),
  eyes = sym.set(eyes),
  region = sym.set(region)
)
sym.table

Description

Create an symbolic_modal type object

Usage

sym.modal(x = character())

Arguments

Value

a symbolic modal

Examples

sym.modal(factor(c("a", "b", "b", "l")))

Description

Symbolic neural networks regression

Usage

sym.nnet(
  formula,
  sym.data,
  method = c("cm", "crm"),
  hidden = c(10),
  threshold = 0.05,
  stepmax = 1e+05
)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Cazes, Chouakria, Diday and Schektman (1997) proposed the Centers and the Tops Methods to extend the well known principal components analysis method to a particular kind of symbolic objects characterized by multi–values variables of interval type.

Usage

sym.pca(sym.data, ...)

## S3 method for class 'symbolic_tbl'
sym.pca(
  sym.data,
  method = c("classic", "tops", "centers", "principal.curves", "optimized.distance",
    "optimized.variance", "fixed"),
  fixed.matrix = NULL,
  ...
)

Arguments

Value

Sym.Components: This a symbolic data table with the interval principal components. As this is a symbolic data table we can apply over this table any other symbolic data analysis method (symbolic propagation).

Sym.Prin.Correlations: This is the interval correlations between the original interval variables and the interval principal components, it can be use to plot the symbolic circle of correlations.

Author(s)

Oldemar Rodriguez Rojas

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.

Chouakria A. (1998) Extension des methodes d'analysis factorialle a des donnees de type intervalle, Ph.D. Thesis, Paris IX Dauphine University.

Makosso-Kallyth S. and Diday E. (2012). Adaptation of interval PCA to symbolic histogram variables, Advances in Data Analysis and Classification July, Volume 6, Issue 2, pp 147-159.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

See Also

sym.histogram.pca

Examples

## Not run: 
data(oils)
res <- sym.pca(oils, "centers")

sym.scatterplot(res$Sym.Components[, 1], res$Sym.Components[, 1],
  labels = TRUE, col = "red", main = "PCA Oils Data"
)
sym.scatterplot3d(res$Sym.Components[, 1], res$Sym.Components[, 2],
  res$Sym.Components[, 3],
  color = "blue", main = "PCA Oils Data"
)
sym.scatterplot.ggplot(res$Sym.Components[, 1], res$Sym.Components[, 2],
  labels = TRUE
)
sym.circle.plot(res$Sym.Prin.Correlations)

res <- sym.pca(oils, "classic")
plot(res, choix = "ind")
plot(res, choix = "var")

data(lynne2)
res <- sym.pca(lynne2, "centers")

sym.scatterplot(res$Sym.Components[, 1], res$Sym.Components[, 2],
  labels = TRUE, col = "red", main = "PCA Lynne Data"
)
sym.scatterplot3d(res$Sym.Components[, 1], res$Sym.Components[, 2],
  res$Sym.Components[, 3],
  color = "blue", main = "PCA Lynne Data"
)
sym.scatterplot.ggplot(res$Sym.Components[, 1], res$Sym.Components[, 2],
  labels = TRUE
)
sym.circle.plot(res$Sym.Prin.Correlations)

data(StudentsGrades)
st <- StudentsGrades
s.pca <- sym.pca(st)
plot(s.pca, choix = "ind")
plot(s.pca, choix = "var")

## End(Not run)

Description

Sym.PCA.Hist.PCA.k.plot

Usage

Sym.PCA.Hist.PCA.k.plot(
  data.sym.df,
  title.graph,
  concepts.name,
  title.x,
  title.y,
  pca.axes
)

Arguments

Value

Concepts projected onto the Principal component chosen

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
ACER.p1<-Sym.PCA.Hist.PCA.k.plot(data.sym.df = pca.hist$Bins.df,
                                    title.graph = " ",
                                    concepts.name = c("ACER"),
                                    title.x = "First Principal Component (84.83%)",
                                    title.y = "Frequency",
                                    pca.axes = 1)

ACER.p1

## End(Not run)

Description

To execute predict method the Center Method (CR) and Center and Range Method (CRM) to Linear regression.

Usage

sym.predict(model, ...)

## S3 method for class 'symbolic_lm_cm'
sym.predict(model, new.sym.data, ...)

## S3 method for class 'symbolic_lm_crm'
sym.predict(model, new.sym.data, ...)

## S3 method for class 'symbolic_glm_cm'
sym.predict(model, new.sym.data, response, ...)

## S3 method for class 'symbolic_glm_crm'
sym.predict(model, new.sym.data, response, ...)

Arguments

Value

sym.predict produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. For type = 'terms' this is a matrix with a column per term and may have an attribute 'constant'

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Examples

data(int_prost_train)
data(int_prost_test)
model <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(model, int_prost_test)
pred.cm

Description

Predict model_gbm_cm model

Usage

## S3 method for class 'symbolic_gbm_cm'
sym.predict(model, new.sym.data, n.trees = 500, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict model_gbm_crm model

Usage

## S3 method for class 'symbolic_gbm_crm'
sym.predict(model, new.sym.data, n.trees = 500, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict model_knn_cm model

Usage

## S3 method for class 'symbolic_knn_cm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict model_knn_crm model

Usage

## S3 method for class 'symbolic_knn_crm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict nnet_cm model

Usage

## S3 method for class 'symbolic_nnet_cm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict nnet_crm model

Usage

## S3 method for class 'symbolic_nnet_crm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict rf_cm model

Usage

## S3 method for class 'symbolic_rf_cm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict rf_crm model

Usage

## S3 method for class 'symbolic_rf_crm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict rt_cm model

Usage

## S3 method for class 'symbolic_rt_cm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict rt_crm model

Usage

## S3 method for class 'symbolic_rt_crm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict model_svm_cm model

Usage

## S3 method for class 'symbolic_svm_cm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Predict model_svm_crm model

Usage

## S3 method for class 'symbolic_svm_crm'
sym.predict(model, new.sym.data, ...)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

sym.quantiles.PCA.plot

Usage

sym.quantiles.PCA.plot(
  histogram.PCA.r,
  concept.names,
  var.names,
  Title,
  axes.x.label,
  axes.y.label,
  label.name
)

Arguments

Value

3D plot

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "PC 1 (84.83%)"
axes.y.label<- "PC 2 (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
plot.3D.HW<-sym.quantiles.PCA.plot(Hardwood.quantiles.PCA,
                                     concept.names,
                                     var.names,
                                     Title,
                                     axes.x.label,
                                     axes.y.label,
                                     label.name)

plot.3D.HW

## End(Not run)

Description

Symbolic Regression with Random Forest

Usage

sym.rf(formula, sym.data, method = c("cm", "crm"), ntree = 500)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

Symbolic Regression Trees

Usage

sym.rt(
  formula,
  sym.data,
  method = c("cm", "crm"),
  minsplit = 20,
  maxdepth = 10
)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

This function could be use to plot two symbolic variables in a X-Y plane.

Usage

sym.scatterplot(sym.var.x, sym.var.y, labels = FALSE, ...)

Arguments

Value

Return a graphics.

Author(s)

Oldemar Rodriguez Rojas

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

See Also

sym.scatterplot3d

Examples

## Not run: 
data(example3)
sym.data <- example3
sym.scatterplot(sym.data[, 3], sym.data[, 7], col = "blue", main = "Main Title")
sym.scatterplot(sym.data[, 1], sym.data[, 4],
  labels = TRUE, col = "blue",
  main = "Main Title"
)
sym.scatterplot(sym.data[, 2], sym.data[, 6],
  labels = TRUE,
  col = "red", main = "Main Title", lwd = 3
)

data(oils)
sym.scatterplot(oils[, 2], oils[, 3],
  labels = TRUE,
  col = "red", main = "Oils Data"
)
data(lynne1)

sym.scatterplot(lynne1[, 2], lynne1[, 1],
  labels = TRUE,
  col = "red", main = "Lynne Data"
)

## End(Not run)

Description

Create an symbolic_set type object

Usage

sym.set(x = NA)

Arguments

Value

a symbolic set

Examples

sym.set(factor(c("a", "b", "b", "l")))

Description

Symbolic Support Vector Machines Regression

Usage

sym.svm(
  formula,
  sym.data,
  method = c("cm", "crm"),
  scale = TRUE,
  kernel = "radial"
)

Arguments

References

Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515

Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347

Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y

Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38

Description

This function applies the UMAP algorithm to a symbolic data table.

Usage

sym.umap(sym.data, ...)

## S3 method for class 'symbolic_tbl'
sym.umap(
  sym.data = NULL,
  config = umap::umap.defaults,
  method = c("naive", "umap-learn"),
  preserve.seed = TRUE,
  ...
)

Arguments

Description

This function get a symbolic variable from a symbolic data table.

Usage

sym.var(sym.data, number.sym.var)

Arguments

Value

Return a symbolic data variable with the following structure:

$N

[1] 7

$var.name

[1] 'F6'

$var.type

[1] '$I'

$obj.names

[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5' 'Case6' 'Case7'

$var.data.vector

F6 F6.1

Case1 0.00 90.00

Case2 -90.00 98.00

Case3 65.00 90.00

Case4 45.00 89.00

Case5 20.00 40.00

Case6 5.00 8.00

Case7 3.14 6.76

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

See Also

sym.obj

Description

Us crime classic data table that can be used to generate symbolic data tables.

Usage

data(USCrime)

Format

An object of class data.frame with 1994 rows and 103 columns.

Source

http://archive.ics.uci.edu/ml/

References

HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.

Examples

## Not run: 
data(USCrime)
us.crime <- USCrime
dim(us.crime)
head(us.crime)
summary(us.crime)
names(us.crime)
nrow(us.crime)
result <- classic.to.sym(us.crime,
  concept = "state",
  variables = c(NumInShelters, NumImmig),
  variables.types = c(
    NumInShelters = type.histogram(),
    NumImmig = type.histogram()
  )
)
result

## End(Not run)

Description

Us crime classic data table genetated from uscrime data.

Usage

data(uscrime_int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 46 rows and 102 columns.

References

Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.

Examples

data(uscrime_int)
car.data <- uscrime_int
res.cm.lasso <- sym.glm(
  sym.data = car.data, response = 102, method = "cm", alpha = 1,
  nfolds = 10, grouped = TRUE
)
plot(res.cm.lasso)
plot(res.cm.lasso$glmnet.fit, "norm", label = TRUE)
plot(res.cm.lasso$glmnet.fit, "lambda", label = TRUE)

pred.cm.lasso <- sym.predict(res.cm.lasso, response = 102, car.data)
RMSE.L(car.data$ViolentCrimesPerPop, pred.cm.lasso)
RMSE.U(car.data$ViolentCrimesPerPop, pred.cm.lasso)
R2.L(car.data$ViolentCrimesPerPop, pred.cm.lasso)
R2.U(car.data$ViolentCrimesPerPop, pred.cm.lasso)
deter.coefficient(car.data$ViolentCrimesPerPop, pred.cm.lasso)

Description

Us crime classic data table genetated from uscrime data.

Usage

data(uscrime_int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 46 rows and 102 columns.

References

Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.

Description

Compute the symbolic variance.

Usage

var(x, ...)

## Default S3 method:
var(x, y = NULL, na.rm = FALSE, use, ...)

## S3 method for class 'symbolic_interval'
var(x, method = c("centers", "interval", "billard"), na.rm = FALSE, ...)

## S3 method for class 'symbolic_tbl'
var(x, ...)

Arguments

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Description

Variance of the principal curve

Usage

variance.princ.curve(data,curve)

Arguments

Value

The variance of the principal curve.

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Vertex of the intervals

Usage

vertex.interval(sym.data)

Arguments

Value

Vertices of the intervals.

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pc

Description

Symbolic data matrix with all the variables of interval type.

Usage

data(VeterinaryData)

Format

$I Height Height $I Weight Weight

1 $I 120.0 180.0 $I 222.2 354.0

2 $I 158.0 160.0 $I 322.0 355.0

3 $I 175.0 185.0 $I 117.2 152.0

4 $I 37.9 62.9 $I 22.2 35.0

5 $I 25.8 39.6 $I 15.0 36.2

6 $I 22.8 58.6 $I 15.0 51.8

7 $I 22.0 45.0 $I 0.8 11.0

8 $I 18.0 53.0 $I 0.4 2.5

9 $I 40.3 55.8 $I 2.1 4.5

10 $I 38.4 72.4 $I 2.5 6.1

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(VeterinaryData)
VeterinaryData

Description

weighted.center.Hist.RSDA

Usage

weighted.center.Hist.RSDA(sym.histogram)

Arguments

Value

Matrix of Weighted Centers

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data(hardwoodBrito)
weighted.center.Hist.RSDA(hardwoodBrito)

## End(Not run)

Description

This function write (save) a symbolic data table from a CSV data file.

Usage

write.sym.table(sym.data, file, sep, dec, row.names = NULL, col.names = NULL)

Arguments

Value

Write in CSV file the symbolic data table.

Author(s)

Oldemar Rodriguez Rojas

References

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

See Also

read.sym.table

Examples

## Not run: 
data(example1)
write.sym.table(example1, file = "temp4.csv", sep = "|",
                dec = ".", row.names = TRUE, col.names = TRUE)
ex1 <- read.sym.table("temp4.csv", header = TRUE,
                       sep = "|", dec = ".", row.names = 1)

## End(Not run)