# predictmeans: Predicted Means of a Linear Model In predictmeans: Calculate Predicted Means for Linear Models

## Description

This function obtains predicted means, SE of means, SED of means, LSDs and plots of means with Stder bar or LSD bar for parametric models such as `aov`, `lm`, `glm`, `gls`, `lme`, and `lmer`. The function also perfomrs pairwise comparisons and permutation tests.

## Usage

 ```1 2 3 4 5``` ```predictmeans(model, modelterm, pairwise=FALSE, atvar=NULL, adj="none", Df=NULL, level=0.05, covariate=NULL, trans = NULL, responsen=NULL, count=FALSE, plotord=NULL, plottitle=NULL, mplot=TRUE, barplot=FALSE, pplot=TRUE, bkplot=TRUE, plot=TRUE, jitterv=0, basesz=12, prtnum=TRUE, newwd=TRUE, permlist=NULL) ```

## Arguments

 `model` Model object returned by `aov`, `lm`, `glm`, `gls`, `lme`, and `lmer`. `modelterm` Name (in "quotes") for indicating which factor term's predicted mean to be calculated. The `modelterm` must be given exactly as it appears in the printed model, e.g. "A" or "A:B". `pairwise` An option for showing pair-wise LSDs and p-values, or not. The default is FALSE. `atvar` When `pairwise = TRUE`, a quoted name indicating within levels of which variable in `modelterm` the multiple comparison will be performed. `adj` Name (in "quote") for indicating a method for adjusting p-values of pairwise comparisons. The choices are "none", "tukey", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY" and "fdr". The default method is "none". `Df` A degree of freedom for calculating LSD. For the above models, Df is obtained from the function automatically. `level` A significant level for calculating LSD. The default value is 0.05. `covariate` A numerical vector to specify values of covariates for calculating predicted means. The default values are the means of the associated covariates. `trans` A function object for calculating the back transformed means, e.g. `trans=exp`. `responsen` Name (in "quotes") of the back transformed response variable in the `model`. `count` An option for indicating the back transformed mean values are counts or not. The default is FALSE. `plotord` A numeric vector specifying the order of plotting for two or three way interaction (e.g. `plotord = c(2, 1, 3)` will put the second variable in `modelterm` on the `X` axis, the first variable as the grouping variable, and the third one as the panel variable). The defaults are `c(1, 2)` and `c(1, 2, 3)` for two and three way interactions. `plottitle` A character vector specifying the main title for plot(s). The default is NULL. `mplot` An option for drawing a means plot, or not. The default is TRUE. `barplot` An option for drawing a bar chart, or not. The default is FALSE. `pplot` An option for drawing a p-values plot, or not when there are more than six p-values. The default is TRUE. `bkplot` An option for drawing back transformed plot, or not. The default is TRUE. `plot` An option for drawing plots, or not. The default is TRUE. `jitterv` A degree of jitter in x and y direction in the back transformed means graph. The default is zero. `basesz` The base font size. The default is 12. `prtnum` An option for printing covariate information on the screen, or not. The default is TRUE. `newwd` A logical variable to indicate whether to print graph in a new window. The default is TRUE. `permlist` A model parameter list produced by the function `permmodels`. When `permlist != NULL`, the option `Df` will be non-functional. This is a key option for pairwise comparisons via permutation tests.

## Value

 `Predicted Means` A table of predicted means. `Standard Error of Means` A table of standard errors of predicted means. `Standard Error of Differences` Standard errors of differences between predicted means. `LSD` Least significant differences between predicted means. `Back Transformed Means` When `trans!=NULL`, a table of back transformed means with CIs are shown. `Pairwise p-value` A matrix with t-values above the diagonal and p-values below the diagonal, or matrix of pairwise comparison p-values for each level of `atvar`.

## Note

The `predictmeans` function becomes confused if a factor or covariate is changed to the other in a model formula. Consequently, formulae that include calls `as.factor`, `factor`, or `numeric` (e.g. `as.factor(income)`) will cause errors. Instead, create the modified variables outside of the model formula (e.g., `fincome <- as.factor(income)`) and then use them in the model formula.

Factors cannot have colons in level names (e.g., `"level:A"`); the `predictmeans` function will confuse the colons with interactions; rename levels to avoid colons.

For `predictmeans` function, it is assumed that methods `coef`, `vcov`, `model.matrix`, `model.frame` and `terms` are available for `model`.

## Author(s)

Dongwen Luo, Siva Ganesh and John Koolaard

## References

Torsten Hothorn, Frank Bretz and Peter Westfall (2008), Simultaneous Inference in General Parametric Models. Biometrical, Journal 50(3), 346–363.

Welham, S., Cullis, B., Gogel, B., Gilm our, A., & Thompson, R. (2004), Prediction in linear mixed models, Australian and New Zealand Journal of Statistics, 46(3), 325-347.

## Examples

 ```1 2 3 4 5 6 7 8``` ``` library(predictmeans) ftable(xtabs(yield ~ Block+Variety+nitro, data=Oats)) Oats\$nitro <- factor(Oats\$nitro) fm <- lme(yield ~ nitro*Variety, random=~1|Block/Variety, data=Oats) # library(lme4) # fm <- lmer(yield ~ nitro*Variety+(1|Block/Variety), data=Oats) predictmeans(fm, "nitro", adj="BH") predictmeans(fm, "nitro:Variety", pair=TRUE, atvar="Variety", adj="BH") ```

predictmeans documentation built on May 29, 2017, 10:39 p.m.