# Pointwise SSA test statistic The function `pointwise_ssa_test_statistic()` calculates and draws the pointwise SSA test statistic. ```r pointwise_ssa_test_statistic( x, plot = TRUE, values = FALSE, type = "l", ylab = "", main = "SSA(t)", ... ) ``` ## Arguments - `x`: a list of length $\ell$ with elements being $n\times p$ matrices of data corresponding to $n$ functional observations measured in $p$ design time points under given experimental conditions. - `plot`: a logical indicating of whether to draw the values of the pointwise SSA test statistic. The default is `TRUE`. - `values`: a logical indicating of whether to return the values of the pointwise SSA test statistic. The default is `FALSE`. - `type`: 1-character string giving the type of plot desired, the same as in the `plot()` function. The default is `"l"` for lines. - `ylab`: a label for the $y$-axis, the same as in the `plot()` function. The default is the empty sign. - `main`: a main title for the plot, the same as in the `plot()` function. The default is `SSA(t)`. - `...`: other graphical parameters, the same as in the `plot()` function. ## Returns If `values = TRUE`, a vector of values of the pointwise SSA test statistic. ## Details For details, see the documentation of the `rmfanova()` function or the paper Kurylo and Smaga (2023). ## Examples ```r # preparation of the DTI data set, for details see Kurylo and Smaga (2023) library(refund) data(DTI) # MS patients DTI_ms <- DTI[DTI$case == 1, ] miss_data <- c() for (i in 1:340) if (any(is.na(DTI_ms$cca[i, ]))) miss_data <- c(miss_data, i) DTI_ms <- DTI_ms[-miss_data, ] DTI_ms_2 <- DTI_ms[DTI_ms$Nscans == 4, ] xx <- vector("list", 4) for (i in 1:4) { xx[[i]] <- DTI_ms_2$cca[DTI_ms_2$visit == i, ] } xx[[1]] <- xx[[1]][-14, ] xx[[3]] <- xx[[3]][-14, ] yy <- xx for (i in seq_len(4)) yy[[i]] <- yy[[i]][1:17, ] # pointwise SSA test statistic pointwise_ssa_test_statistic(yy, xlab = "t", xaxt = "n") axis(1, c(1, 15, 30, 45, 60, 75, 93), labels = c(1, 15, 30, 45, 60, 75, 93)) ``` ## References Martinez-Camblor P., Corral N. (2011) Repeated Measures Analysis for Functional Data. Computational Statistics & Data Analysis 55, 3244–3256. Kurylo K., Smaga L. (2023) Functional repeated measures analysis of variance and its application. Preprint https://arxiv.org/abs/2306.03883

pointwise_ssa_test_statistic function