Some people may advise you to use the "subset" command to extract subsets of a data.frame. ..- attr(*, "assign")= int [1:2] 0 1 ..$ qraux: num [1:2] 1.10 1.04 ..$ pivot: int [1:2] 1 2 ..$ tol : num 1e-07 ..$ rank : int 2 ..- attr(*, "class")= chr "qr" ... The Singular Value Decomposition (SVD) A=Q1*S*Q2 (Q1, Q2: matrices containing the eigenvectors of A*t(A) and t(A)*A; S: diagonal matrix containing the square roots of the eigenvalues of A*t(A) or t(A)*A (they are the same)) which yields, when A is symetrix, its diagonalization in an orthonormal basis; it also used in the computation of the pseudo inverse. $ Assault : int 236 263 294 190 276 204 110 238 335 211 ... $ Rape : num 21.2 44.5 31 19.5 40.6 38.7 11.1 15.8 31.9 25.8 ...

Here are several ways to define them (here, "c" stands for "concatenate"). We shall see later another application to the simulation of non independant normal variables, with a given variance-covariance matrix.

In particular, if you need it, you can write functions that take other functions as argument -- and in case you wonder, yes, you need it. When you call a function you can use the argument names, without any regard to their order (this is very useful for functions that expect many arguments -- in particular arguments with default values).

After the arguments, in the definition of a function, you can put three dots represented the arguments that have not been specified and that can passed through another function (very often, the "plot" function).

# Regression data(cars) # load the "cars" data frame lm( dist ~ speed, data=cars) # Polynomial regression lm( dist ~ poly(speed,3), data=cars) # Regression with splines library(Design) lm( y ~ rcs(x) ) # TODO: Find some data # Logistic regression glm(y ~ x1 x2, family=binomial, data=...) # TODO: Find some data library(Design) lrm(death ~ blood.presure age) # TODO: Find some data # Non linear regression nls( y ~ a b * exp(c * x), start = c(a=1, b=1, c=-1) ) # TODO: Find some data ? There are also many decompositions based on the matrix t(A)*A. - attr(*, "dimnames")=List of 2 ..$ : chr [1:4] "Murder" "Assault" "Urban Pop" "Rape" ..$ : chr [1:4] "Comp.1" "Comp.2" "Comp.3" "Comp.4" - attr(*, "class")= chr "loadings" str(USArrests) `data.frame': 50 obs.

self Start # Principal Component Analysis data(USArrest) princomp( ~ Murder Assault Urban Pop, data=USArrest) # Treillis graphics xyplot( x ~ y | group ) # TODO: Find some data We shall see in a separate section how to transform data frames, because there are several ways of putting the result of an experiment in a table -- but usually, we shall prefer the one with the most rows and the fewer columns. The A=QR decomposition (R: upper triangular, Q: unitary) expresses the Gram-Schmidt orthonormalization of the columns of A -- we can compute it from the LU decomposition of t(A)*A. of 4 variables: $ Murder : num 13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ...

Feb 20, 2007. Once an InsertCommand, UpdateCommand, or DeleteCommand value has been specified, the Enable Inserting, Enable Editing, or Enable Deleting option in the corresponding data Web control s smart tag will become available. To illustrate this, let s take an example from the page we.

This section contains an alphabetical reference for all KML elements defined in KML Version 2.2, as well as elements in the Google extension namespace.

Step 11 - Accessing the Encrypted Data. All the read access users will see the encrypted values while they do a select on table. A user need to have permission to.

Connecting a Form to a Database - Stefan Cameron on Forms Building intelligent forms using Adobe LiveCycle Designer.