curveFitting: Fit longitudinal data

Description Usage Arguments Value References Examples

View source: R/omicslondaHelper.R

Description

Fits longitudinal samples from the same group using negative binomial smoothing splines or LOWESS

Usage

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curveFitting(formula = Count ~ Time, df = "NULL",
  fit.method = "ssgaussian", points = NULL)

Arguments

formula

formula to be passed to the regression model

df

dataframe has the Count, Group, Subject, Time

fit.method

fitting method (ssgaussian)

points

points at which the prediction should happen

Value

a list that contains fitted smoothing spline for each group along with 95

References

Ahmed Metwally (ametwall@stanford.edu)

Examples

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library(SummarizedExperiment)
data("omicslonda_data_example")
omicslonda_se_object_adjusted = adjustBaseline(
                 se_object = omicslonda_data_example$omicslonda_se_object)
se_object = omicslonda_se_object_adjusted[1,]
dt = data.frame(colData(se_object))
dt$Count = as.vector(assay(se_object))
Group = as.character(dt$Group)
group.levels = sort(unique(Group))
gr.1 = as.character(group.levels[1])
gr.2 = as.character(group.levels[2])
df = dt
levels(df$Group) = c(levels(df$Group), "0", "1")
df$Group[which(df$Group == gr.1)] = 0
df$Group[which(df$Group == gr.2)] = 1
group.0 = df[df$Group == 0, ]
group.1 = df[df$Group == 1, ]
points = seq(1, 500)
model = curveFitting(formula = Count ~ Time, df = df, fit.method = "ssgaussian", points = points)

aametwally/OmicsLonDA documentation built on Feb. 10, 2020, 11:47 a.m.