predictmeans: Predicted Means of a Linear Model

In predictmeans: Predicted Means for Linear and Semiparametric Models

Predicted Means of a Linear Model

Description

This function obtains predicted means, SE of means, SED of means, LSDs and plots of means with SE 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

predictmeans(model, modelterm, data=NULL, pairwise=FALSE, atvar=NULL, adj="none", Df=NULL,
  lsd_bar=TRUE, level=NULL, covariate=NULL, meandecr=NULL, letterCI=FALSE, trans = I,
  transOff = 0, responsen=NULL, count=FALSE, plotord=NULL, plottitle=NULL, plotxlab=NULL,
  plotylab=NULL, mplot=TRUE, barplot=FALSE, pplot=TRUE, bkplot=TRUE, plot=TRUE,
  jitterv=0.2, basesz=12, prtnum=TRUE, prtplt=TRUE, newwd=TRUE, permlist=NULL, ncore=3, 
  ndecimal=4)

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 factors and given exactly as it appears in the printed model, e.g. "A" or "A:B".
data	In some cases, you need to provide the data set used in model fitting, especially when you have applied some variable trnasformation in the model.
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". Note that LSD can't be adjusted except for "bonferroni" method.
Df	A degree of freedom for calculating LSD. For the above models, Df is obtained from the function automatically.
lsd_bar	A logical variable to indicate to print an average LSD or SED bar on the means plot. The default is TRUE.
level	A significant level for calculating LSD, CI etc. 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.
meandecr	A logical variable to indicate whether to print letters for multiple comparisons by decreasing order of means in the mean_table. The default is NULL which indicates the mean order follows the associated factor levels.
letterCI	A logical variable to indicate printed letters for multiple comparisons by whether or not CI overlap in the mean_table. The default is FALSE.
trans	A function object for calculating the back transformed means, e.g. trans=exp.
transOff	When you use trans=exp(x+1), then transOff=1, the default is 0.
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.
plotxlab	A character vector specifying the x label for plot(s). The default is NULL.
plotylab	A character vector specifying the y label 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.
prtplt	An option for printing plots 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.
ncore	Number of core for parallel computing when permlist != NULL, the default value is 3.
ndecimal	An option for specifying number of decimal point to be print at predicted means table. The default is 4.

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.
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.
mean_table	A summary of predicted means result including 'Predicted means', 'Standard error', 'Df' and 'CIs'. When trans!=NULL or trans!=I, a table of back transformed means with CIs are also shown.
predictmeansPlot	ggplot of predicted means.
predictmeansBKPlot	ggplot of back transformed means.
predictmeansBarPlot	gg bar plot of predicted means.
p_valueMatrix	p_value matrix for pairwise comparison.

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., Gilmour, A., & Thompson, R. (2004), Prediction in linear mixed models, Australian and New Zealand Journal of Statistics, 46(3), 325-347.

Vanessa, C. (05 October 2022), Confidence tricks: the 83.4% confidence interval for comparing means, https://vsni.co.uk/blogs/confidence_trick.

Examples

  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)
# fm <- lmer(yield ~ nitro*Variety+(1|Block/Variety), data=Oats)
  predictmeans(fm, "nitro", adj="BH")
  predictmeans(fm, "nitro:Variety", atvar="Variety", adj="BH")
  predictout <- predictmeans(fm, "nitro:Variety", atvar="Variety", adj="BH", barplot=TRUE)
  names(predictout)
  print(predictout$predictmeansPlot)
  print(predictout$predictmeansBarPlot)

predictmeans documentation built on Oct. 20, 2023, 5:07 p.m.