IC: Information Criteria

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Various Information Criteria for "lm" and "glm" objects.

Usage

IC(
  model,
  criteria = c("AIC", "BIC", "CAIC", "KIC", "HQIC", "FIC", "ICOMP_IFIM_C1",
    "ICOMP_PEU_C1", "ICOMP_PEU_LN_C1", "CICOMP_C1"),
  ...
)

Arguments

model	a "lm" or "glm" object or object list
criteria	a vector of criteria names. Can be set to respective numbers. Possible criteria names at the moment are: 1 = "AIC" 2 = "AIC4" 3 = "BIC" 4 = "BICadj" 5 = "BICQ" 6 = "CAIC" 7 = "CAICF" 8 = "FIC" 9 = "GCV" 10 = "HBIV" 11 = "GQIC" 12 = "IBIC" 13 = "ICOMP_IFIM_CF" 14 = "ICOMP_IFIM_C1" 15 = "ICOMP_IFIM_C1F" 16 = "ICOMP_IFIM_C1R" 17 = "ICOMP_PEU_CF" 18 = "ICOMP_PEU_C1" 19 = "ICOMP_PEU_C1F" 20 = "ICOMP_PEU_C1R" 21 = "ICOMP_PEU_LN_CF" 22 = "ICOMP_PEU_LN_C1" 23 = "ICOMP_PEU_LN_C1F" 24 = "ICOMP_PEU_LN_C1R" 25 = "CICOMP_CF" 26 = "CICOMP_C1" 27 = "CICOMP_C1F" 28 = "CICOMP_C1R" 29 = "JIC" 30 = "KIC" 31 = "KICC" 32 = "SPBIC"
...	additional parameters. Currently none.

Details

Calculates Various Information Criteria for "lm" and "glm" objects. model can be a list. If it is a list, function returns a matrix of selected information criteria for all models.

Value

Information criteria of the model(s) for selected criteria

Examples

x1 <- rnorm(100, 3, 2)
x2 <- rnorm(100, 5, 3)
x3 <- rnorm(100, 67, 5)
err <- rnorm(100, 0, 4)

## round so we can use it for Poisson regression
y <- round(3 + 2*x1 - 5*x2 + 8*x3 + err)

m1 <- lm(y~x1 + x2 + x3)
m2 <- glm(y~x1 + x2 + x3, family = "gaussian")
m3 <- glm(y~x1 + x2 + x3, family = "poisson")

IC(model = m1, criteria = 1:32)
IC(model = list(lm = m1,
               glm = m2,
               glm_pois = m3), criteria = 1:32)

ICglm documentation built on Nov. 12, 2021, 1:06 a.m.