plsRglm (version 0.8.2)

simul_data_UniYX_binom: Data generating function for univariate binomial plsR models

Description

This function generates a single univariate binomial response value $Y$ and a vector of explanatory variables $(X_1,\ldots,X_{totdim})$ drawn from a model with a given number of latent components.

Usage

simul_data_UniYX_binom(totdim, ncomp, link="logit", offset = 0)

Arguments

totdim

Number of columns of the X vector (from ncomp to hardware limits)

ncomp

Number of latent components in the model (from 2 to 6)

link

Character specification of the link function in the mean model (mu). Currently, "logit", "probit", "cloglog", "cauchit", "log", "loglog" are supported. Alternatively, an object

offset

Offset on the linear scale

Value

• vector
  $(Y,X_1,\ldots,X_{totdim})$

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a modification of a R

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576. http://dx.doi.org/10.1080/03610918508812458 Baibing Li, Julian Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89. http://dx.doi.org/10.1016/S0169-7439(02)00051-5

See Also

simul_data_UniYX

Examples

layout(matrix(1:6,nrow=2))

# logit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4)))[,1])
# probit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="probit")))[,1])
# cloglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cloglog")))[,1])
# cauchit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cauchit")))[,1])
# loglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="loglog")))[,1])
# log link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="log")))[,1])

layout(1)


layout(matrix(1:6,nrow=2))

# logit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,offset=5)))[,1])
# probit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="probit",offset=5)))[,1])
# cloglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cloglog",offset=5)))[,1])
# cauchit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cauchit",offset=5)))[,1])
# loglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="loglog",offset=5)))[,1])
# log link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="log",offset=5)))[,1])

layout(1)


layout(matrix(1:6,nrow=2))

# logit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,offset=-5)))[,1])
# probit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="probit",offset=-5)))[,1])
# cloglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cloglog",offset=-5)))[,1])
# cauchit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cauchit",offset=-5)))[,1])
# loglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="loglog",offset=-5)))[,1])
# log link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="log",offset=-5)))[,1])

layout(1)