Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width=7, fig.height=5
)
library(dplyr)
library(ggplot2)
theme_set(theme_bw())
library(rticulate)
## ----load-data----------------------------------------------------------------
library(rticulate)
data(tongue)
tongue
## ----polar-place--------------------------------------------------------------
tongue_it05 <- filter(tongue, speaker == "it05", vowel == "a", fan_line < 38) %>% droplevels()
polar_place <- polar_gam(
Y ~
s(X, by = c2_place),
data = tongue_it05
)
summary(polar_place)
## ----plot-smooths-------------------------------------------------------------
plot_polar_smooths(
polar_place,
X,
c2_place
) +
theme(legend.position = "top")
## ----polar-multi--------------------------------------------------------------
tongue_it05 <- filter(tongue, speaker == "it05", fan_line < 38) %>% droplevels()
polar_multi <- polar_gam(
Y ~
s(X, by = c2_place) +
s(X, by = vowel),
data = tongue_it05
)
summary(polar_multi)
## ----plot-smooths-2-----------------------------------------------------------
plot_polar_smooths(
polar_multi,
X,
c2_place,
facet_terms = vowel
) +
theme(legend.position = "top")
## ----polar-place-2------------------------------------------------------------
polar_2 <- polar_gam(
Y ~
s(X) +
s(X, by = c2_place) +
s(TR_abs_velocity, k = 6) +
ti(X, TR_abs_velocity, k = c(9, 6)) +
s(X, word, bs = "fs"),
data = tongue_it05
)
summary(polar_2)
## ----place-pred---------------------------------------------------------------
polar_pred <- predict_polar_gam(
polar_2,
values = list(TR_abs_velocity = seq(2, 24, 5)),
exclude_terms = "s(X,word)"
) %>%
filter(word == "paca") # filter data by choosing any value for word
polar_pred
## ----place-pred-plot, fig.width=7, fig.height=5-------------------------------
polar_pred %>%
ggplot(aes(X, Y, colour = as.factor(TR_abs_velocity), linetype = as.factor(TR_abs_velocity))) +
geom_path() +
facet_grid(c2_place ~ .)
## ----ci-data------------------------------------------------------------------
polar_multi_p <- predict_polar_gam(
polar_multi
)
ci_data <- predict_polar_gam(
polar_multi,
return_ci = TRUE,
)
## ----place-pred-ci------------------------------------------------------------
polar_multi_p %>%
ggplot(aes(X, Y)) +
geom_polygon(data = ci_data, aes(CI_X, CI_Y, group = c2_place), alpha = 0.1) +
geom_path(aes(colour = c2_place)) +
facet_grid(. ~ vowel) +
theme(legend.position = "top")
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