VCA (version 1.3.1)

lsmeans: Least Squares Means of Fixed Effects.

Description

Computes Least Squares Means (LS Means) of fixed effects for fitted mixed models of class 'VCA'.

Usage

lsmeans(obj, var = NULL, type = c("simple", "complex"), ddfm = c("contain", "residual", "satterthwaite"), at = NULL, contr.mat = FALSE, quiet = FALSE)

Arguments

obj

(VCA) object having at least one fixed effect

var

(character) string specifying a fixed effects variable for which LS Means should be computed, defaults to all fixed effects, i.e. for each level of a fixed effects variable ls means will be computed

type

(character) "simple" = fast version of computing LS means

ddfm

(character) string specifying the method used for computing the degrees of freedom of the t-statistic. Only used when type="complex". Available methods are "contain", "residual", and "satterthwaite".

at

(list) where each element corresponds either to a (numeric) covariable or to a factor-variable for which the weighting scheme should be adjusted. See details section for a thorough description of how argument 'at' works and also see the examples.

contr.mat

(logical) TRUE = the LS Means generating contrast-matrix will be added to the result as attribute contrasts

quiet

(logical) TRUE = suppress warning messages, e.g. for non-estimable contrasts

Value

(matrix) with LS Means of fixed effects and respective standard errors, in case of 'type="complex"'

Details

Function computes LS Means of fixed effects and their corresponding standard errors. In case of setting argument 'type' equal to "complex" (or any abbreviation) a $t$-test is performed on each LS Mean, returning degrees of freedom, t-statistic and corresponding p-values. One can choose from one of three denominator degrees of freedom ('ddfm')-methods.

Actually, function test.fixef is called with the "no intercept" version of the fitted model. The "complex" option is significantly slower for unbalanced designs (see test.fixef for details). In case that the 'VarCov' element of the 'VCA' object already exists (calling vcovVC), which is the most time consuming part, results can be obtained in less amount of time.

Standard Errors of LS Means are computed as $T * P * T'$, where $T$ is the LS Means generating contrast matrix and $P$ is the variance-covariance matrix of fixed effects.

Argument at can be used to modify the values of covariables when computing LS Means and/or to apply different weighting schemes for (fixed) factor varialbes in the model, e.g. when the prevelance of factor-levels differs from a uniform distribution. Usually, if the weighting scheme is not modified, each factor-level will contribute $1/N$ to the LS Mean, where $N$ corresponds to the number of factor-levels.

Covariables have to be specified as 'name=value', where value can be a vector of length > 1. Each value will be evaluated for each row of the original LS Means contrast matrix. If multiple covariables are specified, the i-th element of covariable 1 will be matched with the i-th element of covariable(s) 2...M, where $M$ is the number of covariables in the model.

To apply a different weighting scheme for factor-variables one has to specify 'factor-name=c(level-name_1=value_1, level-name_2=value_2, ..., level-name_N=value_N)'. The sum of all 'value_i' elements must be equal to 1, otherwise, this factor-variable will be skipped issuing a warning. If any levels 'level-name_i' cannot be found for factor-variable 'factor-name', this variable will also be skipped and a warning will be issued. See the examples section to get an impression of how this works.

Examples

#
## Not run: 
# data(dataEP05A2_2)
# fit1 <- anovaMM(y~day/(run), dataEP05A2_2)
# lsmeans(fit1)
# lsmeans(fit1,, "complex")
# 
# # a more complex model
# data(VCAdata1)
# fit2 <- anovaMM(y~(lot+device)/(day)/(run), VCAdata1[VCAdata1$sample==2,])
# lsmeans(fit2, "lot")
# lsmeans(fit2, "device", "complex")
# 
# # pre-computed 'VarCov' element saves time
# system.time(lsm1 <- lsmeans(fit2, "device", "complex"))
# fit2$VarCov <- vcovVC(fit2)
# system.time(lsm2 <- lsmeans(fit2, "device", "complex"))
# lsm1
# lsm2
# 
# # simulate some random data
# set.seed(212)
# id <- rep(1:10,10)
# x <- rnorm(200)
# time <- sample(1:5,200,replace=T)
# y <- rnorm(200)+time
# snp <- sample(0:1,200,replace=T)
# dat <- data.frame(id=id,x=x,y=y,time=time,snp=snp)
# dat$snp <- as.factor(dat$snp)
# dat$id <- as.factor(dat$id)
# dat$time <- as.numeric(dat$time)
# dat$sex <- gl(2, 100, labels=c("Male", "Female"))
# dat$y <- dat$y + rep(rnorm(2, 5, 1), c(100, 100))
# 
# fit3 <- remlMM(y~snp+time+snp:time+sex+(id)+(id):time, dat)
# 
# # comute standard LS Means for variable "snp"
# lsmeans(fit3, var="snp")
# lsmeans(fit3, var="snp", type="c")    # comprehensive output
# 
# # compute LS Means at timepoints 1, 2, 3, 4
# # Note: original LS Means are always part of the output
# lsmeans(fit3, var="snp", at=list(time=1:4))
# 
# # compute LS Means with different weighting scheme
# # for factor-variable 'sex'
# lsmeans(fit3, var="snp", at=list(sex=c(Male=.3, Female=.7)))
# 
# # combine covariables at some value and altering the
# # weighting scheme
# lsmeans(fit3, var="snp", at=list(time=1:4, sex=c(Male=.3, Female=.7)))
# 
# # now with comprehensive output and requesting the
# # LS Means generating contrast matrix
# lsmeans(fit3, var="snp", type="complex", contr.mat=TRUE,
#         at=list(time=1:4, sex=c(Male=.3, Female=.7)))
# ## End(Not run)