growthcurve {statmod} | R Documentation |
Do all pairwise comparisons between groups of growth curves using a permutation test.
compareGrowthCurves(group,y,levels=NULL,nsim=100,fun=meanT,times=NULL,verbose=TRUE,adjust="holm") compareTwoGrowthCurves(group,y,nsim=100,fun=meanT)
group |
vector or factor indicating group membership. Missing values are allowed in compareGrowthCurves but not in compareTwoGrowthCurves . |
y |
matrix of response values with rows for individuals and columns for times. The number of rows must agree with the length of group . Missing values are allowed. |
levels |
a character vector containing the identifiers of the groups to be compared. By default all groups with two more more members will be compared. |
nsim |
number of permutations to estimated p-values. |
fun |
the statistic used to measure the distance between two groups of growth curves. Default to a mean t-statistic. |
times |
a numeric vector containing the column numbers on which the groups should be compared. By default all the columns are used. |
verbose |
should progress results be printed? |
adjust |
method used to adjust for multiple testing, see p.adjust . |
compareTwoGrowthCurves
performs a permutation test of the difference between two groups of growth curves.
compareGrowthCurves
does all pairwise comparisons between two or more groups of growth curves.
Accurate p-values can be obtained by setting nsim
to some large value, nsim=10000
say.
compareTwoGrowthCurves
returns a list with two components, stat
and p.value
, containing the observed statistics and the estimated p-value. compareGrowthCurves
returns a data frame with components
Group1 |
name of first group in a comparison |
Group2 |
name of second group in a comparison |
Stat |
observed value of the statistic |
P.Value |
estimated p-value |
adj.P.Value |
p-value adjusted for multiple testing |
Gordon Smyth
compareGrowthCurves
, compareTwoGrowthCurves
# A example with only one time data(PlantGrowth) compareGrowthCurves(PlantGrowth$group,as.matrix(PlantGrowth$weight)) # Can make p-values more accurate by nsim=10000