factorplot.Rd
This function calculates all pairwise difference from the input data. The
input data can be the result of a GLM (produced with glm
), a
multinomial logit model (produced with multinom
from the nnet
package), a general linear hypothesis test (produced with glht
from the multcomp package), an object of class eff
from the
effects
package or any vector of values and a corresponding
variance-covariance matrix.
factorplot(obj, adjust.method = "none", ...) # S3 method for glm factorplot(obj, adjust.method = "none", order = "natural", factor.variable = NULL, pval = 0.05, two.sided = TRUE, ...) # S3 method for lm factorplot(obj, adjust.method = "none", order = "natural", factor.variable = NULL, pval = 0.05, two.sided = TRUE, ...) # S3 method for summary.glht factorplot(obj, ...) # S3 method for glht factorplot(obj, adjust.method = "none", pval = 0.05, ...) # S3 method for sims factorplot(obj, adjust.method = "none", order = "natural", pval = 0.05, ...) # S3 method for default factorplot(obj, adjust.method = "none", order = "natural", var, resdf = Inf, pval = 0.05, two.sided = TRUE, ...) # S3 method for eff factorplot(obj, adjust.method = "none", order = "natural", pval = 0.05, two.sided = TRUE, ordby = NULL, ...) # S3 method for multinom factorplot(obj, adjust.method = "none", order = "natural", variable, pval = 0.05, two.sided = TRUE, ...)
obj | An object of class |
---|---|
adjust.method | For objects of class |
... | Additional arguments to be passed to
|
order | One of ‘natural’, ‘alph’, or ‘size’ indicating how the levels of the factor should be ordered for presentation. The ‘natural’ option (the default) leaves the levels as they are in the factor contrasts. ‘alph’ sorts the levels alphabetically and ‘size’ sorts the levels by size of coefficient. |
factor.variable | String containing the name of the factor for which
pairwise coefficient differences will be calculated (if a |
pval | The (uncorrected) Type I error probability required, default = 0.05 |
two.sided | Logical argument indicating whether the hypothesis test should be against a two-sided alternative if TRUE (default) or a one-sided alternative if FALSE |
var | Variance-covariance matrix to be used if |
resdf | Residual degrees of freedom used as the degrees of freedom for
the t-distribution from which p-values will be generated if |
ordby | For objects of class |
variable | String containing the name of the column of the model matrix
for which pairwise differences will be calculated if a |
An upper-triangular matrix of pairwise differences between row and column levels of the factor
An upper-triangular matrix of standard errors of the pairwise differences represented in b.diff
An upper-triangular matrix of uncorrected (one-sided) p-values corresponding to the entries of b.diff
The p-value specified in the command
This function calculates pairwise differences that can be passed to a novel plotting method that does not suffer from some of the same problems as floating/quasi confidence intervals and is easier to apprehend immediately than a compact letter display.
While the factorplot function and its print and summary methods work equally
well regardless of the number of levels in the factor.variable
, the
plot function automatically scales the resulting graph to the appropriate
size, but will be less useful as the number of contrasts gets large (e.g., >
30). If more than one factor covariate is present and the
factor.variable
option is NULL, the function generates a text-based
menu in the R GUI that will allow the users to pick the term for which they
want to calculate the results.
Easton, D.F., J. Peto and G.A.G. Babiker. 1991. Floating
absolute risk: An alternative to relative risk in survival and case control
analysis avoiding an arbitrary reference group. Statistics in
Medicine 10: 1025--1035.
Firth, David and Renee X. de Menzes.
2004. Quasi-variances. Biometrika 91.1: 65--80.
Plummer,
M. 2004. Improved estimates of floating absolute risk. Statistics in
Medicine 23: 93--104.
## for lm/glm x <- as.factor(round(runif(1000, .5,5.5))) levels(x) <- paste("lab", 1:20, sep="") X <- model.matrix(~x) Y <- X %*% rnorm(ncol(X),0,4) + rnorm(1000) mod <- lm(Y ~ x) fp <- factorplot(mod, factor.variable="x", pval = 0.05, order="alph") ## for glht library(multcomp) mod.glht <- glht(mod, linfct = mcp('x' = 'Tukey')) fp2 <- factorplot(mod.glht, adjust.method='single-step') ## for vector of values b <- c(0, mod$coef[-1]) v <- rbind(0, cbind(0, vcov(mod)[-1,-1])) names(b) <- colnames(v) <- rownames(v) <- mod$xlevels[["x"]] fp3 <- factorplot(b, var=v, resdf=mod$df.residual) ## for multinomial logit data(france) library(nnet) multi.mod <- multinom(vote ~ retnat + lrself + male + age, data=france)#> # weights: 35 (24 variable) #> initial value 872.315349 #> iter 10 value 655.272636 #> iter 20 value 559.902797 #> iter 30 value 551.176433 #> final value 551.169697 #> convergedfp4 <- factorplot(multi.mod, variable="lrself")