Construct a User-Specified Contrast Matrix
Construct a user-specified contrast matrix.
make.contrasts(contr, how.many = ncol(contr))
contr |
vector or matrix specifying contrasts (one per row). |
how.many |
dimensions of the desired contrast matrix. This must equal the number of levels of the target factor variable. |
This function converts human-readable contrasts into the form that R requires for computation.
Specifying a contrast row of the form
c(1,0,0,-1) creates a contrast that will compare the mean of the
first group with the mean of the fourth group.
make.contrasts returns a matrix with dimensions
(how.many, how.many) containing the specified contrasts
augmented (if necessary) with orthogonal "filler" contrasts.
Gregory R. Warnes greg@warnes.net
lm, contrasts,
contr.treatment, contr.poly,
Computation and testing of General Linear Hypothesis:
glh.test, Computation and testing of estimable functions
of model coefficients: estimable, Estimate and Test
Contrasts for a previously fit linear model: fit.contrast
set.seed(4684)
y <- rnorm(100)
x.true <- rnorm(100, mean=y, sd=0.25)
x <- factor(cut(x.true,c(-4,-1.5,0,1.5,4)))
reg <- lm(y ~ x)
summary(reg)
# Mirror default treatment contrasts
test <- make.contrasts(rbind( c(-1,1,0,0), c(-1,0,1,0), c(-1,0,0,1) ))
lm( y ~ x, contrasts=list(x = test ))
# Specify some more complicated contrasts
# - mean of 1st group vs mean of 4th group
# - mean of 1st and 2nd groups vs mean of 3rd and 4th groups
# - mean of 1st group vs mean of 2nd, 3rd and 4th groups
cmat <- rbind( "1 vs 4" =c(-1, 0, 0, 1),
"1+2 vs 3+4"=c(-1/2,-1/2, 1/2, 1/2),
"1 vs 2+3+4"=c(-3/3, 1/3, 1/3, 1/3))
summary(lm( y ~ x, contrasts=list(x=make.contrasts(cmat) )))
# or
contrasts(x) <- make.contrasts(cmat)
summary(lm( y ~ x ) )
# or use contrasts.lm
reg <- lm(y ~ x)
fit.contrast( reg, "x", cmat )
# compare with values computed directly using group means
gm <- sapply(split(y,x),mean)
gm
#
# Example for Analysis of Variance
#
set.seed(03215)
Genotype <- sample(c("WT","KO"), 1000, replace=TRUE)
Time <- factor(sample(1:3, 1000, replace=TRUE))
data <- data.frame(y, Genotype, Time)
y <- rnorm(1000)
data <- data.frame(y, Genotype, as.factor(Time))
# Compute Contrasts & obtain 95% confidence intervals
model <- aov( y ~ Genotype + Time + Genotype:Time, data=data )
fit.contrast( model, "Genotype", rbind("KO vs WT"=c(-1,1) ), conf=0.95 )
fit.contrast( model, "Time",
rbind("1 vs 2"=c(-1,1,0),
"2 vs 3"=c(0,-1,1)
),
conf=0.95 )
cm.G <- rbind("KO vs WT"=c(-1,1) )
cm.T <- rbind("1 vs 2"=c(-1,1,0),
"2 vs 3"=c(0,-1,1) )
# Compute contrasts and show SSQ decompositions
model <- model <- aov( y ~ Genotype + Time + Genotype:Time, data=data,
contrasts=list(Genotype=make.contrasts(cm.G),
Time=make.contrasts(cm.T) )
)
summary(model, split=list( Genotype=list( "KO vs WT"=1 ),
Time = list( "1 vs 2" = 1,
"2 vs 3" = 2 ) ) )Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.