require(Hmisc) n <- 500; set.seed(88) sex <- factor(sample(c("female","male"), n, TRUE)) age <- rnorm(n, 50, 10) height <- rnorm(n, 1.7, 0.5) type <- factor(sample(c('A', 'B'), n, TRUE)) dbase= data.frame(sex, age, height, type)
s <- summaryM(age + height + type ~ sex , data=dbase, overall=TRUE, test=TRUE) html(s, prmsd = TRUE, npct='slash', caption="Descriptive Statistics", round = 2, digits=2, prtest='P', pdig=2)
# From Lauren Samuels set.seed(1) d <- expand.grid(x1=c('A', 'B'), x2=c('a', 'b', 'c')) d$y <- runif(nrow(d)) htmlVerbatim(d) # htmlVerbatim is in Hmisc h <- html( summaryM(x2 + y ~ x1, data= d, test=TRUE, overall=TRUE, continuous=6 ), caption="Descriptive stats and tests of between-group differences for all primary and secondary neuroimaging outcomes", exclude1=FALSE, digits=2, prmsd=TRUE, npct="slash") cat(as.character(h), file='z.html', sep='\n') h
## Example taken from help file for summaryM options(digits=3) set.seed(173) sex <- factor(sample(c("m","f"), 500, rep=TRUE)) country <- factor(sample(c('US', 'Canada'), 500, rep=TRUE)) age <- rnorm(500, 50, 5) sbp <- rnorm(500, 120, 12) label(sbp) <- 'Systolic BP' units(sbp) <- 'mmHg' treatment <- factor(sample(c("Drug","Placebo"), 500, rep=TRUE)) treatment[1] sbp[1] <- NA # Generate a 3-choice variable; each of 3 variables has 5 possible levels symp <- c('Headache','Stomach Ache','Hangnail', 'Muscle Ache','Depressed') symptom1 <- sample(symp, 500,TRUE) symptom2 <- sample(symp, 500,TRUE) symptom3 <- sample(symp, 500,TRUE) Symptoms <- mChoice(symptom1, symptom2, symptom3, label='Primary Symptoms') table(as.character(Symptoms)) # Produce separate tables by country f <- summaryM(age + sex + sbp + Symptoms ~ treatment + country, groups='treatment', test=TRUE) html(f, npct='slash', middle.bold=TRUE, prmsd=TRUE)
getHdata(pbc) # load('~/data/teaching/pbc.sav') s5 <- summaryM(bili + albumin + stage + protime + sex + age + spiders ~ drug, data=pbc, test=TRUE) html(s5, npct='both', insert.bottom = "Polly")
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