Construction of Multiple Contrast Profiles
Calculates signed root deviance profiles given a glm or lm object. The profiled parameters of interest are defined by providing a contrast matrix.
mcprofile(object, CM, control = mcprofileControl(), grid = NULL) # S3 method for glm mcprofile(object, CM, control = mcprofileControl(), grid = NULL) # S3 method for lm mcprofile(object, CM, control = mcprofileControl(), grid = NULL)
Arguments
| object | An object of class |
|---|---|
| CM | A contrast matrix for the definition of parameter linear combinations ( |
| control | A list with control arguments. See |
| grid | A matrix or list with profile support coordinates. Each column of the matrix or slot in a list corresponds to a row in the contrast matrix, each row of the grid matrix or element of a numeric vector in each list slot corresponds to a candidate of the contrast parameter. If NULL (default), a grid is found automatically similar to function |
Value
An object of class mcprofile. The slot srdp contains the profiled signed root deviance statistics. The optpar slot contains a matrix with profiled parameter estimates.
Details
The profiles are calculates separately for each row of the contrast matrix. The profiles are calculated by constrained IRWLS optimization, implemented in function orglm, using the quadratic programming algorithm of package quadprog.
See also
profile.glm, glht, contrMat, confint.mcprofile, summary.mcprofile, solve.QP
Examples
####################################### ## cell transformation assay example ## ####################################### str(cta)#> 'data.frame': 80 obs. of 2 variables: #> $ conc: num 0 0 0 0 0 0 0 0 0 0 ... #> $ foci: int 0 1 0 0 0 0 0 0 0 2 ...## change class of cta$conc into factor cta$concf <- factor(cta$conc, levels=unique(cta$conc)) ggplot(cta, aes(y=foci, x=concf)) + geom_boxplot() + geom_dotplot(binaxis = "y", stackdir = "center", binwidth = 0.2) + xlab("concentration")# glm fit assuming a Poisson distribution for foci counts # parameter estimation on the log link # removing the intercept fm <- glm(foci ~ concf-1, data=cta, family=poisson(link="log")) ### Comparing each dose to the control by Dunnett-type comparisons # Constructing contrast matrix library(multcomp) CM <- contrMat(table(cta$concf), type="Dunnett") # calculating signed root deviance profiles (dmcp <- mcprofile(fm, CM))#> #> Multiple Contrast Profiles #> #> Estimate Std.err #> 0.01 - 0 0.511 0.730 #> 0.03 - 0 -0.405 0.913 #> 0.1 - 0 1.386 0.645 #> 0.3 - 0 2.159 0.610 #> 1 - 0 2.730 0.596 #> 3 - 0 2.813 0.594 #> 10 - 0 2.979 0.592 #># plot profiles plot(dmcp)# confidence intervals (ci <- confint(dmcp))#> #> mcprofile - Confidence Intervals #> #> level: 0.95 #> adjustment: single-step #> #> Estimate lower upper #> 0.01 - 0 0.511 -1.2489 2.55 #> 0.03 - 0 -0.405 -3.0517 1.87 #> 0.1 - 0 1.386 -0.0172 3.31 #> 0.3 - 0 2.159 0.8961 4.04 #> 1 - 0 2.730 1.5194 4.59 #> 3 - 0 2.813 1.6083 4.67 #> 10 - 0 2.979 1.7835 4.83 #>plot(ci)
