Experiment
compute_panel_outcome <- function(data, scales){
data |>
mutate(x = row_number())
}
library(statexpress)
ggplot(mtcars) +
aes(y = mpg) +
geom_point(stat = qstat_panel(compute_panel_outcome))
model <- lm(mpg~ disp, mtcars)
model$fitted.values
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 23.00544 23.00544 25.14862 18.96635
## Hornet Sportabout Valiant Duster 360 Merc 240D
## 14.76241 20.32645 14.76241 23.55360
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 23.79677 22.69220 22.69220 18.23272
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## 18.23272 18.23272 10.14632 10.64090
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 11.46520 26.35622 26.47987 26.66946
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 24.64992 16.49345 17.07046 15.17456
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 13.11381 26.34386 24.64168 25.68030
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## 15.13335 23.62366 17.19410 24.61283
compute_panel_model <- function(data, scales, formula = y ~ .){
y_var_names <- c()
for(i in 1:ncol(data)){
is_identical <- identical(data$y, data[,i])
var_name <- names(data)[i]
y_var_names <- c(y_var_names, if(is_identical){var_name}else{c()})
}
y_var_names <- y_var_names[y_var_names != "y"]
# if(length(y_var_names) > 0){
model <- lm(formula, data = data |> select(-group, - PANEL, -y_var_names))
# }else{model <- lm(formula, data = data |> select(-group, - PANEL))}
data$y <- model$fitted.values
data |>
mutate(x = row_number())
}
model <- lm(mpg ~ mpg, mtcars)
## Warning in model.matrix.default(mt, mf, contrasts): the response appeared on
## the right-hand side and was dropped
## Warning in model.matrix.default(mt, mf, contrasts): problem with term 1 in
## model.matrix: no columns are assigned
model$residuals
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 0.909375 0.909375 2.709375 1.309375
## Hornet Sportabout Valiant Duster 360 Merc 240D
## -1.390625 -1.990625 -5.790625 4.309375
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 2.709375 -0.890625 -2.290625 -3.690625
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## -2.790625 -4.890625 -9.690625 -9.690625
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## -5.390625 12.309375 10.309375 13.809375
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 1.409375 -4.590625 -4.890625 -6.790625
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## -0.890625 7.209375 5.909375 10.309375
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## -4.290625 -0.390625 -5.090625 1.309375
mtcars |>
select(y = mpg, t = disp, x = mpg, group = 2, PANEL = 2) |>
compute_panel_model()
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(y_var_names)
##
## # Now:
## data %>% select(all_of(y_var_names))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## y t x group PANEL
## Mazda RX4 23.00544 160.0 1 6 6
## Mazda RX4 Wag 23.00544 160.0 2 6 6
## Datsun 710 25.14862 108.0 3 4 4
## Hornet 4 Drive 18.96635 258.0 4 6 6
## Hornet Sportabout 14.76241 360.0 5 8 8
## Valiant 20.32645 225.0 6 6 6
## Duster 360 14.76241 360.0 7 8 8
## Merc 240D 23.55360 146.7 8 4 4
## Merc 230 23.79677 140.8 9 4 4
## Merc 280 22.69220 167.6 10 6 6
## Merc 280C 22.69220 167.6 11 6 6
## Merc 450SE 18.23272 275.8 12 8 8
## Merc 450SL 18.23272 275.8 13 8 8
## Merc 450SLC 18.23272 275.8 14 8 8
## Cadillac Fleetwood 10.14632 472.0 15 8 8
## Lincoln Continental 10.64090 460.0 16 8 8
## Chrysler Imperial 11.46520 440.0 17 8 8
## Fiat 128 26.35622 78.7 18 4 4
## Honda Civic 26.47987 75.7 19 4 4
## Toyota Corolla 26.66946 71.1 20 4 4
## Toyota Corona 24.64992 120.1 21 4 4
## Dodge Challenger 16.49345 318.0 22 8 8
## AMC Javelin 17.07046 304.0 23 8 8
## Camaro Z28 15.17456 350.0 24 8 8
## Pontiac Firebird 13.11381 400.0 25 8 8
## Fiat X1-9 26.34386 79.0 26 4 4
## Porsche 914-2 24.64168 120.3 27 4 4
## Lotus Europa 25.68030 95.1 28 4 4
## Ford Pantera L 15.13335 351.0 29 8 8
## Ferrari Dino 23.62366 145.0 30 6 6
## Maserati Bora 17.19410 301.0 31 8 8
## Volvo 142E 24.61283 121.0 32 4 4
library(statexpress)
ggplot(mtcars) +
aes(y = mpg) +
geom_point(stat = qstat_panel(compute_panel_outcome))
last_plot() +
aes_all("cyl") +
# geom model
geom_point(stat = qstat_panel(compute_panel_model), color = "blue") +
NULL
last_plot() +
aes_all("gear")
last_plot() +
aes_all(names(mtcars)[2:11]) # the rest but not y
last_plot() +
aes_all(names(mtcars)) # uh-oh y predicts y perfectly...
library(statexpress)
ggplot(mtcars) +
aes(y = mpg) +
# geom_outcome
geom_point(stat = qstat_panel(compute_panel_outcome)) +
ggplyr::aes_from_data() +
# geom_lm_predictions
geom_point(stat = qstat_panel(compute_panel_model), color = "blue")
layer_data(i = 2) |> head()
## mpg cyl disp hp drat wt qsec vs am gear carb y PANEL group x
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 22.59951 1 -1 1
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 22.11189 1 -1 2
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 26.25064 1 -1 3
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 21.23740 1 -1 4
## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 17.69343 1 -1 5
## 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 20.38304 1 -1 6
## shape colour fill size alpha stroke
## 1 19 blue NA 1.5 NA 0.5
## 2 19 blue NA 1.5 NA 0.5
## 3 19 blue NA 1.5 NA 0.5
## 4 19 blue NA 1.5 NA 0.5
## 5 19 blue NA 1.5 NA 0.5
## 6 19 blue NA 1.5 NA 0.5
library(statexpress)
ggplot(mtcars) +
aes(y = mpg, color = factor(cyl)) +
# geom_outcome
geom_point(stat = qstat_panel(compute_panel_outcome)) +
ggplyr::aes_from_data() +
# geom_lm_predictions
geom_point(stat = qstat_panel(compute_panel_model),
color = "blue",
formula = y ~ drat + cyl)
layer_data(i = 2) |> head()
## mpg cyl disp hp drat wt qsec vs am gear carb y PANEL group x
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 21.12431 1 -1 1
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 21.12431 1 -1 2
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 25.99774 1 -1 3
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 19.58929 1 -1 4
## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 14.75330 1 -1 5
## 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 18.99025 1 -1 6
## shape colour fill size alpha stroke
## 1 19 blue NA 1.5 NA 0.5
## 2 19 blue NA 1.5 NA 0.5
## 3 19 blue NA 1.5 NA 0.5
## 4 19 blue NA 1.5 NA 0.5
## 5 19 blue NA 1.5 NA 0.5
## 6 19 blue NA 1.5 NA 0.5