Description Usage Arguments Details Value See Also Examples
View source: R/report.lsmeans.R
Creates a desc object for "LS Means" statistics reporting.
For more examples see the website: ClinReport website
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lsm |
emmGrid object (result of a |
at.row |
Character. Passed to spacetable function. Used to space the results per levels of the mentioned variable |
infer |
A vector of one or two logical values. Passed to |
round |
Numeric. Specify the number of digits to round the statistics |
x1 |
deprecated |
x2 |
deprecated |
x3 |
deprecated |
x1.name |
deprecated |
x2.name |
deprecated |
x3.name |
deprecated |
data |
deprecated |
contrast |
deprecated |
contrast.name |
deprecated |
type |
deprecated |
transpose |
Logical. If TRUE Statistics will be reported in columns |
y.label |
Character Indicates the label for y parameter to be displayed in the title of the table |
You can produce formatted Least Square Means table for up to 3 factors. It doesn't work for quantitative covariates.
See examples below.
A desc object that can be used by the report.doc
function.
report.quali
emmeans
report.doc
desc
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library(lme4)
data(datafake)
#Simple lm model
mod=lm(Petal.Width~Species,data=iris)
raw.lsm=emmeans(mod,~Species)
report.lsmeans(raw.lsm)
# You can display the Statistics in columns
report.lsmeans(raw.lsm,transpose=TRUE)
# In case of just one intercept
mod=glm(Species~1,data=iris,family=binomial)
raw.lsm=emmeans(mod,~1)
report.lsmeans(raw.lsm)
# Display statistics in columns
report.lsmeans(raw.lsm,transpose=TRUE)
#Mixed model example using lme4
mod=lmer(y_numeric~GROUP+TIMEPOINT+GROUP*TIMEPOINT+(1|SUBJID),data=datafake)
raw.lsm=emmeans(mod,~GROUP|TIMEPOINT)
report.lsmeans(lsm=raw.lsm,at="TIMEPOINT")
# Display statistics in columns
report.lsmeans(lsm=raw.lsm,at="TIMEPOINT",transpose=TRUE)
# LM model with specific contrast
warp.lm <- lm(breaks ~ wool+tension+wool:tension, data = warpbreaks)
warp.emm <- emmeans(warp.lm, ~ tension | wool)
contr=contrast(warp.emm, "trt.vs.ctrl", ref = "M")
report.lsmeans(lsm=contr,at="wool")
# Display statistics in columns
report.lsmeans(lsm=contr,at="wool",transpose=TRUE)
# Cox model
library(survival)
data(time_to_cure)
fit <- coxph(Surv(time, status) ~ Group, data = time_to_cure)
em=emmeans(fit,~Group,type="response")
pairs=pairs(em,adjust="none",exclude="Untreated")
pairs
report.lsmeans(pairs)
# Display statistics in columns
report.lsmeans(pairs,transpose=TRUE)
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