cAIC4 (version 1.0)

stepcAIC: Function to stepwise select the (generalized) linear mixed model fitted via (g)lmer() or (generalized) additive (mixed) model fitted via gamm4() with the smallest cAIC.

Description

The step function searches the space of possible models in a greedy manner, where the direction of the search is specified by the argument direction. If direction = "forward" / = "backward", the function adds / exludes random effects until the cAIC can't be improved further. In the case of forward-selection, either a new grouping structure, new slopes for the random effects or new covariates modeled nonparameterically must be supplied to the function call. If direction = "both", the greedy search is alternating between forward and backward steps, where the direction is changed after each step

Usage

stepcAIC(
  object,
  numberOfSavedModels = 1,
  groupCandidates = NULL,
  slopeCandidates = NULL,
  fixEfCandidates = NULL,
  numberOfPermissibleSlopes = 2,
  allowUseAcross = FALSE,
  allowCorrelationSel = FALSE,
  allowNoIntercept = FALSE,
  direction = "backward",
  trace = FALSE,
  steps = 50,
  keep = NULL,
  numCores = 1,
  data = NULL,
  returnResult = TRUE,
  calcNonOptimMod = TRUE,
  bsType = "tp",
  digits = 2,
  printValues = "caic",
  ...
)

Arguments

object

object returned by [lme4]{lmer}, [lme4]{glmer} or [gamm4]{gamm4}

numberOfSavedModels

integer defining how many additional models to be saved during the step procedure. If 1 (DEFAULT), only the best model is returned. Any number k greater 1 will return the k best models. If 0, all models will be returned (not recommended for larger applications).

groupCandidates

character vector containing names of possible grouping variables for new random effects. Group nesting must be specified manually, i.e. by listing up the string of the groups in the manner of lme4. For example groupCandidates = c("a", "b", "a/b").

slopeCandidates

character vector containing names of possible new random effects

fixEfCandidates

character vector containing names of possible (non-)linear fixed effects in the GAMM; NULL for the (g)lmer-use case

numberOfPermissibleSlopes

how much slopes are permissible for one grouping variable

allowUseAcross

allow slopes to be used in other grouping variables

allowCorrelationSel

logical; FALSE does not allow correlations of random effects to be (de-)selected (default)

allowNoIntercept

logical; FALSE does not allow random effects without random intercept

direction

character vector indicating the direction ("both","backward","forward")

trace

logical; should information be printed during the execution of stepcAIC?

steps

maximum number of steps to be considered

keep

list($fixed,$random) of formulae; which splines / fixed (fixed) or random effects (random) to be kept during selection; specified terms must be included in the original model

numCores

the number of cores to be used in calculations; parallelization is done by using parallel::mclapply

data

data.frame supplying the data used in object. data must also include variables, which are considered for forward updates.

returnResult

logical; whether to return the result (best model and corresponding cAIC)

calcNonOptimMod

logical; if FALSE, models which failed to converge are not considered for cAIC calculation

bsType

type of splines to be used in forward gamm4 steps

digits

number of digits used in printing the results

printValues

what values of c("cll", "df", "caic", "refit") to print in the table of comparisons

...

further options for cAIC call

Value

if returnResult is TRUE, a list with the best model finalModel, additionalModels if numberOfSavedModels was specified and the corresponding cAIC bestCAIC is returned.

Note that if trace is set to FALSE and returnResult is also FALSE, the function call may not be meaningful

Details

Note that the method can not handle mixed models with uncorrelated random effects and does NOT reduce models to such, i.e., the model with (1 + s | g) is either reduced to (1 | g) or (0 + s | g) but not to (1 + s || g).

Examples

# NOT RUN {
(fm3 <- lmer(strength ~ 1 + (1|sample) + (1|batch), Pastes))

fm3_step <- stepcAIC(fm3, direction = "backward", trace = TRUE, data = Pastes)

fm3_min <- lm(strength ~ 1, data=Pastes)

fm3_min_step <- stepcAIC(fm3_min, groupCandidates = c("batch", "sample"), 
direction="forward", data=Pastes, trace=TRUE)
fm3_min_step <- stepcAIC(fm3_min, groupCandidates = c("batch", "sample"), 
direction="both", data=Pastes, trace=TRUE)
# try using a nested group effect which is actually not nested -> warning
fm3_min_step <- stepcAIC(fm3_min, groupCandidates = c("batch", "sample", "batch/sample"), 
                         direction="both", data=Pastes, trace=TRUE)

Pastes$time <- 1:dim(Pastes)[1]
fm3_slope <- lmer(data=Pastes, strength ~ 1 + (1 + time | cask))

fm3_slope_step <- stepcAIC(fm3_slope,direction="backward", trace=TRUE, data=Pastes)



fm3_min <- lm(strength ~ 1, data=Pastes)

fm3_min_step <- stepcAIC(fm3_min,groupCandidates=c("batch","sample"),
direction="forward", data=Pastes,trace=TRUE)



fm3_inta <- lmer(strength ~ 1 + (1|sample:batch), data=Pastes)

fm3_inta_step <- stepcAIC(fm3_inta,groupCandidates=c("batch","sample"),
direction="forward", data=Pastes,trace=TRUE)

fm3_min_step2 <- stepcAIC(fm3_min,groupCandidates=c("cask","batch","sample"),
direction="forward", data=Pastes,trace=TRUE)

fm3_min_step3 <- stepcAIC(fm3_min,groupCandidates=c("cask","batch","sample"),
direction="both", data=Pastes,trace=TRUE)

# }
# NOT RUN {
fm3_inta_step2 <- stepcAIC(fm3_inta,direction="backward", 
data=Pastes,trace=TRUE)
# }
# NOT RUN {
##### create own example


na <- 20
nb <- 25
n <- 400
a <- sample(1:na,400,replace=TRUE)
b <- factor(sample(1:nb,400,replace=TRUE))
x <- runif(n)
y <- 2 + 3 * x + a*.02 + rnorm(n) * .4
a <- factor(a)
c <- interaction(a,b)
y <- y + as.numeric(as.character(c))*5
df <- data.frame(y=y,x=x,a=a,b=b,c=c)

smallMod <- lm(y ~ x)

# }
# NOT RUN {
# throw error
stepcAIC(smallMod, groupCandidates=c("a","b","c"), data=df, trace=TRUE, returnResult=FALSE)

smallMod <- lm(y ~ x, data=df)

# throw error
stepcAIC(smallMod, groupCandidates=c("a","b","c"), data=df, trace=TRUE, returnResult=FALSE)

# get it all right
mod <- stepcAIC(smallMod, groupCandidates=c("a","b","c"), 
                data=df, trace=TRUE, 
                direction="forward", returnResult=TRUE)

# make some more steps...
stepcAIC(smallMod, groupCandidates=c("a","b","c"), data=df, trace=TRUE, 
         direction="both", returnResult=FALSE)

mod1 <- lmer(y ~ x + (1|a), data=df)

stepcAIC(mod1, groupCandidates=c("b","c"), data=df, trace=TRUE, direction="forward")
stepcAIC(mod1, groupCandidates=c("b","c"), data=df, trace=TRUE, direction="both")



mod2 <- lmer(y ~ x + (1|a) + (1|c), data=df)

stepcAIC(mod2, data=df, trace=TRUE, direction="backward")

mod3 <- lmer(y ~ x + (1|a) + (1|a:b), data=df)

stepcAIC(mod3, data=df, trace=TRUE, direction="backward")

# }
# NOT RUN {
# }