{math} R# Documentation

math

require(R);

#' the R# math module
imports "math" from "Rlapack";

the R# math module

the R# math module

.NET clr type export
yfit: WeightedFit

加权拟合的结果


.NET clr function exports
diff_entropy

measure similarity between two data vector via entropy difference
solve.RK4

solve a given ODE system
deSolve

solve a given ODE system
hist

Do fixed width cut bins
gini
bootstrap

Non-Parametric Bootstrapping

See Efron and Tibshirani (1993) for details on this function.
loess

loess fit
glm

Fitting Generalized Linear Models

glm is used to fit generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution.
binomial

Family Objects for Models

Family objects provide a convenient way to specify the details of the models used by functions such as glm. See the documentation for glm for the details on how such model fitting takes place.
as.lm_call

cast the list data dump from the R lm result
lm

Fitting Linear Models

do linear modelling, lm is used to fit linear models. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (although aov may provide a more convenient interface for these).
as.formula

create a lambda function based on the lm result.
predict

Model Predictions

predict is a generic function for predictions from the results of various model fitting functions. The function invokes particular methods which depend on the class of the first argument.
cosine

Evaluate cos similarity of two vector the given vector x and y should be contains the elements in the same length.
sim

Create a similarity matrix
RamerDouglasPeucker

Ramer-Douglas-Peucker algorithm for curve fitting with a PolyLine

The Ramer-Douglas-Peucker algorithm for reducing the number of points on a curve. If there are no more than two points it does not make sense to simplify. In this case the input is returned without further checks of `x` and `y`. In particular, the input is not checked for `NA` values. 

 RamerDouglasPeucker(x = c(0, 1, 3, 5), y = c(2, 1, 0, 1), epsilon = 0.5)

curve_fit

Use non-linear least squares to fit a function, f, to data. Assumes ydata = f(xdata, args) + eps.
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