curveFitting {OmicsLonDA} | R Documentation |
Fits longitudinal samples from the same group using negative binomial smoothing splines or LOWESS
curveFitting(formula = Count ~ Time, df = "NULL", fit.method = "ssgaussian", points = NULL)
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 |
a list that contains fitted smoothing spline for each group along with 95
Ahmed Metwally (ametwall@stanford.edu)
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)