The Number of Predictors, Coefficients, and Observations

n.par returns the number of regression parameters, covariates and observations present in X based on the selected family.

Usage

n.par(X, family = c("gaussian", "binomial", "survival"))

Arguments

X

design matrix of dimension \(n \times p\), where \(n\) is the number of samples observed, and \(p\) is the number of predictors/covariables. It could be a matrix or a list of matrices.

family

a description of the error distribution used to specify the model. This should be a character string, either “gaussian”, “binomial”, or “survival”. Can be abbreviated. By default the gaussian family is used.

Details

orig.names and covar.names are the same if the all covariates in X are continuous. However, if there are at least one categorical variable in X with more than two categories, they are different.

Value

n.par returns a list containing the following components:

n.reg.par

the number of regression parameters;

n.covar

the number of covariates;

n.sample

the number of samples/observations;

orig.names

the original names of the variables (without including the names of dummy variables);

covar.names

the names of the variables (together with the names of any dummy variables, if applicable).

Author

Hassan Pazira
Maintainer: Hassan Pazira hassan.pazira@radboudumc.nl

Examples

#--------------------
# family = "gaussian"
#--------------------

X0 <- data.frame(x1 = rnorm(50),                     # standard normal variable
                 x2 = sample(0:2, 50, replace=TRUE), # categorical variable
                 x3 = sample(0:1, 50, replace=TRUE)) # dichotomous variable
n.par(X0) # without dummy variables
#> $n.reg.par
#> [1] 3
#> 
#> $n.covar
#> [1] 3
#> 
#> $n.sample
#> [1] 50
#> 
#> $orig.names
#> [1] "x1" "x2" "x3"
#> 
#> $covar.names
#> [1] "x1" "x2" "x3"
#> 
X0$x2 <- as.factor(X0$x2)
X0$x3 <- as.factor(X0$x3)
n.par(X0)  # with dummy variables
#> $n.reg.par
#> [1] 4
#> 
#> $n.covar
#> [1] 3
#> 
#> $n.sample
#> [1] 50
#> 
#> $orig.names
#> [1] "x1" "x2" "x3"
#> 
#> $covar.names
#> [1] "x1"  "x21" "x22" "x31"
#> 

X1 <- data.frame(Intercept = rep(1,30),
                 x1 = rnorm(30),                     # continuous variable
                 x2 = sample(0:2, 30, replace=TRUE)) # categorical variable
n.par(X1) # without dummy variables
#> $n.reg.par
#> [1] 2
#> 
#> $n.covar
#> [1] 2
#> 
#> $n.sample
#> [1] 30
#> 
#> $orig.names
#> [1] "x1" "x2"
#> 
#> $covar.names
#> [1] "x1" "x2"
#> 
X1$x2  <- as.factor(X1$x2)
n.par(X1) # without dummy variables
#> $n.reg.par
#> [1] 3
#> 
#> $n.covar
#> [1] 2
#> 
#> $n.sample
#> [1] 30
#> 
#> $orig.names
#> [1] "x1" "x2"
#> 
#> $covar.names
#> [1] "x1"  "x21" "x22"
#> 

# a list of two data sets:
X01 <- list(X0, X1)
n.par(X01)
#> $n.reg.par
#> [1] 4 3
#> 
#> $n.covar
#> [1] 3 2
#> 
#> $n.sample
#> [1] 50 30
#> 
#> $orig.names
#> $orig.names[[1]]
#> [1] "x1" "x2" "x3"
#> 
#> $orig.names[[2]]
#> [1] "x1" "x2"
#> 
#> 
#> $covar.names
#> $covar.names[[1]]
#> [1] "x1"  "x21" "x22" "x31"
#> 
#> $covar.names[[2]]
#> [1] "x1"  "x21" "x22"
#> 
#>