Description Usage Arguments Details Value Author(s) References See Also Examples
Performs simultaneous tests for several ratios of linear combinations of treatment means in a normal one-way layout, assuming normal distribution of the data allowing heterogeneous variances.
1 2 3 4 5 |
formula |
A formula specifying a numerical response and a grouping factor (e.g., response ~ treatment) |
data |
A dataframe containing the response and group variable |
type |
type of contrast, with the following options:
Note: type is ignored if Num.Contrast and Den.Contrast are specified by the user (See below). |
base |
a single integer specifying the control (i.e. denominator) group for the Dunnett contrasts, ignored otherwise |
alternative |
a character string:
|
Margin.vec |
a single numerical value or vector of Margins under the null hypotheses, default is 1 |
FWER |
a single numeric value specifying the family-wise error rate to be controlled |
Num.Contrast |
Numerator contrast matrix, where columns correspond to groups and rows correspond to contrasts |
Den.Contrast |
Denominator contrast matrix, where columns correspond to groups and rows correspond to contrasts |
names |
a logical value: if TRUE, the output will be named according to names of user defined contrast or factor levels |
The associated ratio test statistic T[i] has a t-distribution. Multiplicity adjustment is achieved by using quantiles of r r-variate t-distributions, which differ in the degree of freedom and share the correlation structure. The compariso-specific degrees of freedom are derived using the approximation according to Satterthwaite (1946).
An object of class simtest.ratio containing:
estimate |
a (named) vector of estimated ratios |
teststat |
a (named) vector of the calculated test statistics |
Num.Contrast |
the numerator contrast matrix |
Den.Contrast |
the denominator contrast matrix |
CorrMat |
the correlation matrix of the multivariate t-distribution calculated under the null hypotheses |
critical.pt |
the equicoordinate critical value of the multi-variate t-distribution for a specified FWER |
p.value.raw |
a (named) vector of unadjusted p-values |
p.value.adj |
a (named) vector of p-values adjusted for multiplicity |
Margin.vec |
the vector of margins under the null hypotheses |
and some other input arguments.
Mario Hasler
Simultaneous tests (adjusted p-values)
Hasler, M. and Hothorn, L.A. (2008): Multiple contrast tests in the presence of heteroscedasticity. Biometrical Journal 50, 793-800.
Unadjusted tests (raw p-values)
Hasler M, Vonk R, Hothorn LA (2007). Assessing non-inferiority of a new treatment in a three-arm trial in the presence of heteroscedasticity. Statistics in Medicine 27, 490-503.
Satterthwaite, FE (1946). An approximate distribution of estimates of variance components. Biometrics 2, 110-114.
sci.ratioVH for corresponding confidence intervals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ###############################################
data(Mutagenicity, package="mratios")
boxplot(MN~Treatment, data=Mutagenicity)
## Not run:
simtest.ratioVH(MN~Treatment, data=Mutagenicity,
type="Dunnett", base=6, Margin.vec=1.2, alternative="less")
###############################################
# Unadjusted confidence intervals for multiple ratios
# of means assuming heterogeneous group variances.
# The following code produces the results given in Table
# V of Hasler, Vonk and Hothorn (2007).
# The upper confidence limits in Table V can produced
# by calling:
# Mutagenicity of the doses of the new compound,
# expressed as ratio (DoseX-Vehicle)/(Cyclo25-Vehicle):
# Check the order of the factor levels:
levels(Mutagenicity$Treatment)
# numerators:
NC<-rbind(
"Hydro30-Vehicle"=c(0,0,1,0,0,-1),
"Hydro50-Vehicle"=c(0,0,0,1,0,-1),
"Hydro75-Vehicle"=c(0,0,0,0,1,-1),
"Hydro100-Vehicle"=c(0,1,0,0,0,-1)
)
DC<-rbind(
"Cyclo25-Vehicle"=c(1,0,0,0,0,-1),
"Cyclo25-Vehicle"=c(1,0,0,0,0,-1),
"Cyclo25-Vehicle"=c(1,0,0,0,0,-1),
"Cyclo25-Vehicle"=c(1,0,0,0,0,-1)
)
colnames(NC)<-colnames(DC)<-levels(Mutagenicity$Treatment)
NC
DC
# The raw p-values are those presented in Table V:
simtest.ratioVH(formula=MN~Treatment, data=Mutagenicity,
Num.Contrast=NC, Den.Contrast=DC,
alternative="less", Margin.vec=0.5, FWER=0.05)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.