| Title: | Modelling Functions that Work with the Pipe |
|---|---|
| Description: | Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation. |
| Authors: | Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd] |
| Maintainer: | Hadley Wickham <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.11.9000 |
| Built: | 2024-11-24 04:55:28 UTC |
| Source: | https://github.com/tidyverse/modelr |
Add predictions to a data frame
add_predictions(data, model, var = "pred", type = NULL) spread_predictions(data, ..., type = NULL) gather_predictions(data, ..., .pred = "pred", .model = "model", type = NULL)add_predictions(data, model, var = "pred", type = NULL) spread_predictions(data, ..., type = NULL) gather_predictions(data, ..., .pred = "pred", .model = "model", type = NULL)
data |
A data frame used to generate the predictions. |
model |
|
var |
The name of the output column, default value is |
type |
Prediction type, passed on to |
... |
|
.pred, .model
|
The variable names used by |
A data frame. add_prediction adds a single new column,
with default name pred, to the input data.
spread_predictions adds one column for each model. gather_predictions
adds two columns .model and .pred, and repeats the input rows for each
model.
df <- tibble::tibble( x = sort(runif(100)), y = 5 * x + 0.5 * x ^ 2 + 3 + rnorm(length(x)) ) plot(df) m1 <- lm(y ~ x, data = df) grid <- data.frame(x = seq(0, 1, length = 10)) grid %>% add_predictions(m1) m2 <- lm(y ~ poly(x, 2), data = df) grid %>% spread_predictions(m1, m2) grid %>% gather_predictions(m1, m2)df <- tibble::tibble( x = sort(runif(100)), y = 5 * x + 0.5 * x ^ 2 + 3 + rnorm(length(x)) ) plot(df) m1 <- lm(y ~ x, data = df) grid <- data.frame(x = seq(0, 1, length = 10)) grid %>% add_predictions(m1) m2 <- lm(y ~ poly(x, 2), data = df) grid %>% spread_predictions(m1, m2) grid %>% gather_predictions(m1, m2)
This merges a one- or two-sided formula f with the
right-hand sides of all formulas supplied in ....
add_predictors(f, ..., fun = "+")add_predictors(f, ..., fun = "+")
f |
A formula. |
... |
Formulas whose right-hand sides will be merged to
|
fun |
A function name indicating how to merge the right-hand sides. |
f <- lhs ~ rhs add_predictors(f, ~var1, ~var2) # Left-hand sides are ignored: add_predictors(f, lhs1 ~ var1, lhs2 ~ var2) # fun can also be set to a function like "*": add_predictors(f, ~var1, ~var2, fun = "*")f <- lhs ~ rhs add_predictors(f, ~var1, ~var2) # Left-hand sides are ignored: add_predictors(f, lhs1 ~ var1, lhs2 ~ var2) # fun can also be set to a function like "*": add_predictors(f, ~var1, ~var2, fun = "*")
Add residuals to a data frame
add_residuals(data, model, var = "resid") spread_residuals(data, ...) gather_residuals(data, ..., .resid = "resid", .model = "model")add_residuals(data, model, var = "resid") spread_residuals(data, ...) gather_residuals(data, ..., .resid = "resid", .model = "model")
data |
A data frame used to generate the residuals |
model, var
|
|
... |
|
.resid, .model
|
The variable names used by |
A data frame. add_residuals adds a single new column,
.resid, to the input data. spread_residuals adds
one column for each model. gather_predictions adds two columns
.model and .resid, and repeats the input rows for
each model.
df <- tibble::tibble( x = sort(runif(100)), y = 5 * x + 0.5 * x ^ 2 + 3 + rnorm(length(x)) ) plot(df) m1 <- lm(y ~ x, data = df) df %>% add_residuals(m1) m2 <- lm(y ~ poly(x, 2), data = df) df %>% spread_residuals(m1, m2) df %>% gather_residuals(m1, m2)df <- tibble::tibble( x = sort(runif(100)), y = 5 * x + 0.5 * x ^ 2 + 3 + rnorm(length(x)) ) plot(df) m1 <- lm(y ~ x, data = df) df %>% add_residuals(m1) m2 <- lm(y ~ poly(x, 2), data = df) df %>% spread_residuals(m1, m2) df %>% gather_residuals(m1, m2)
n bootstrap replicates.Generate n bootstrap replicates.
bootstrap(data, n, id = ".id")bootstrap(data, n, id = ".id")
data |
A data frame |
n |
Number of bootstrap replicates to generate |
id |
Name of variable that gives each model a unique integer id. |
A data frame with n rows and one column: strap
Other resampling techniques:
resample_bootstrap(),
resample_partition(),
resample()
library(purrr) boot <- bootstrap(mtcars, 100) models <- map(boot$strap, ~ lm(mpg ~ wt, data = .)) tidied <- map_df(models, broom::tidy, .id = "id") hist(subset(tidied, term == "wt")$estimate) hist(subset(tidied, term == "(Intercept)")$estimate)library(purrr) boot <- bootstrap(mtcars, 100) models <- map(boot$strap, ~ lm(mpg ~ wt, data = .)) tidied <- map_df(models, broom::tidy, .id = "id") hist(subset(tidied, term == "wt")$estimate) hist(subset(tidied, term == "(Intercept)")$estimate)
crossv_kfold splits the data into k exclusive partitions,
and uses each partition for a test-training split. crossv_mc
generates n random partitions, holding out test of the
data for training. crossv_loo performs leave-one-out
cross-validation, i.e., n = nrow(data) training partitions containing
n - 1 rows each.
crossv_mc(data, n, test = 0.2, id = ".id") crossv_kfold(data, k = 5, id = ".id") crossv_loo(data, id = ".id")crossv_mc(data, n, test = 0.2, id = ".id") crossv_kfold(data, k = 5, id = ".id") crossv_loo(data, id = ".id")
data |
A data frame |
n |
Number of test-training pairs to generate (an integer). |
test |
Proportion of observations that should be held out for testing (a double). |
id |
Name of variable that gives each model a unique integer id. |
k |
Number of folds (an integer). |
A data frame with columns test, train, and .id.
test and train are list-columns containing resample() objects.
The number of rows is n for crossv_mc(), k for crossv_kfold()
and nrow(data) for crossv_loo().
cv1 <- crossv_kfold(mtcars, 5) cv1 library(purrr) cv2 <- crossv_mc(mtcars, 100) models <- map(cv2$train, ~ lm(mpg ~ wt, data = .)) errs <- map2_dbl(models, cv2$test, rmse) hist(errs)cv1 <- crossv_kfold(mtcars, 5) cv1 library(purrr) cv2 <- crossv_mc(mtcars, 100) models <- map(cv2$train, ~ lm(mpg ~ wt, data = .)) errs <- map2_dbl(models, cv2$test, rmse) hist(errs)
To visualise a model, it is very useful to be able to generate an
evenly spaced grid of points from the data. data_grid helps you
do this by wrapping around tidyr::expand().
data_grid(data, ..., .model = NULL)data_grid(data, ..., .model = NULL)
data |
A data frame |
... |
Variables passed on to |
.model |
A model. If supplied, any predictors needed for the model
not present in |
seq_range() for generating ranges from continuous
variables.
data_grid(mtcars, vs, am) # For continuous variables, seq_range is useful data_grid(mtcars, mpg = mpg) data_grid(mtcars, mpg = seq_range(mpg, 10)) # If you supply a model, missing predictors will be filled in with # typical values mod <- lm(mpg ~ wt + cyl + vs, data = mtcars) data_grid(mtcars, .model = mod) data_grid(mtcars, cyl = seq_range(cyl, 9), .model = mod)data_grid(mtcars, vs, am) # For continuous variables, seq_range is useful data_grid(mtcars, mpg = mpg) data_grid(mtcars, mpg = seq_range(mpg, 10)) # If you supply a model, missing predictors will be filled in with # typical values mod <- lm(mpg ~ wt + cyl + vs, data = mtcars) data_grid(mtcars, .model = mod) data_grid(mtcars, cyl = seq_range(cyl, 9), .model = mod)
fit_with() is a pipe-friendly tool that applies a list of
formulas to a fitting function such as stats::lm().
The list of formulas is typically created with formulas().
fit_with(data, .f, .formulas, ...)fit_with(data, .f, .formulas, ...)
data |
A dataset used to fit the models. |
.f |
A fitting function such as |
.formulas |
A list of formulas specifying a model. |
... |
Additional arguments passed on to |
Assumes that .f takes the formula either as first argument
or as second argument if the first argument is data. Most
fitting functions should fit these requirements.
# fit_with() is typically used with formulas(). disp_fits <- mtcars %>% fit_with(lm, formulas(~disp, additive = ~drat + cyl, interaction = ~drat * cyl, full = add_predictors(interaction, ~am, ~vs) )) # The list of fitted models is named after the names of the list of # formulas: disp_fits$full # Additional arguments are passed on to .f mtcars %>% fit_with(glm, list(am ~ disp), family = binomial)# fit_with() is typically used with formulas(). disp_fits <- mtcars %>% fit_with(lm, formulas(~disp, additive = ~drat + cyl, interaction = ~drat * cyl, full = add_predictors(interaction, ~am, ~vs) )) # The list of fitted models is named after the names of the list of # formulas: disp_fits$full # Additional arguments are passed on to .f mtcars %>% fit_with(glm, list(am ~ disp), family = binomial)
formulas() creates a list of two-sided formulas by merging a
unique left-hand side to a list of right-hand sides.
formulas(.response, ...) formulae(.response, ...)formulas(.response, ...) formulae(.response, ...)
.response |
A one-sided formula used as the left-hand side of all resulting formulas. |
... |
List of formulas whose right-hand sides will be merged
to |
# Provide named arguments to create a named list of formulas: models <- formulas(~lhs, additive = ~var1 + var2, interaction = ~var1 * var2 ) models$additive # The formulas are created sequentially, so that you can refer to # previously created formulas: formulas(~lhs, linear = ~var1 + var2, hierarchical = add_predictors(linear, ~(1 | group)) )# Provide named arguments to create a named list of formulas: models <- formulas(~lhs, additive = ~var1 + var2, interaction = ~var1 * var2 ) models$additive # The formulas are created sequentially, so that you can refer to # previously created formulas: formulas(~lhs, linear = ~var1 + var2, hierarchical = add_predictors(linear, ~(1 | group)) )
Add a reference line (ggplot2).
geom_ref_line(h, v, size = 2, colour = "white")geom_ref_line(h, v, size = 2, colour = "white")
h, v
|
Position of horizontal or vertical reference line |
size |
Line size |
colour |
Line colour |
You might have heard that taller people earn more. Is it true? You can try and answer the question by exploring this dataset extracted from the National Longitudinal Study, which is sponsored by the U.S. Bureau of Labor Statistics.
heightsheights
Yearly income. The top two percent of values were averaged and that average was used to replace all values in the top range.
Height, in inches
Weight, in pounds
Age, in years, between 47 and 56.
Marital status
Sex
Years of education
Percentile score on Armed Forces Qualification Test.
This contains data as at 2012.
This is a thin wrapper around stats::model.matrix() which
returns a tibble. Use it to determine how your modelling formula is
translated into a matrix, an thence into an equation.
model_matrix(data, formula, ...)model_matrix(data, formula, ...)
data |
A data frame |
formula |
A modelling formula |
... |
Other arguments passed onto |
A tibble.
model_matrix(mtcars, mpg ~ cyl) model_matrix(iris, Sepal.Length ~ Species) model_matrix(iris, Sepal.Length ~ Species - 1)model_matrix(mtcars, mpg ~ cyl) model_matrix(iris, Sepal.Length ~ Species) model_matrix(iris, Sepal.Length ~ Species - 1)
Three summaries are immediately interpretible on the scale of the response variable:
rmse() is the root-mean-squared-error
mae() is the mean absolute error
qae() is quantiles of absolute error.
Other summaries have varying scales and interpretations:
mape() mean absolute percentage error.
rsae() is the relative sum of absolute errors.
mse() is the mean-squared-error.
rsquare() is the variance of the predictions divided by the
variance of the response.
mse(model, data) rmse(model, data) mae(model, data) rsquare(model, data) qae(model, data, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)) mape(model, data) rsae(model, data)mse(model, data) rmse(model, data) mae(model, data) rsquare(model, data) qae(model, data, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)) mape(model, data) rsae(model, data)
model |
A model |
data |
The dataset |
probs |
Numeric vector of probabilities |
mod <- lm(mpg ~ wt, data = mtcars) mse(mod, mtcars) rmse(mod, mtcars) rsquare(mod, mtcars) mae(mod, mtcars) qae(mod, mtcars) mape(mod, mtcars) rsae(mod, mtcars)mod <- lm(mpg ~ wt, data = mtcars) mse(mod, mtcars) rmse(mod, mtcars) rsquare(mod, mtcars) mae(mod, mtcars) qae(mod, mtcars) mape(mod, mtcars) rsae(mod, mtcars)
This NA handler ensures that those models that support the
na.action parameter do not silently drop missing values. It
wraps around stats::na.exclude() so that there is one
prediction/residual for input row. To apply it globally, run
options(na.action = na.warn).
na.warn(object)na.warn(object)
object |
A data frame |
df <- tibble::tibble( x = 1:10, y = c(5.1, 9.7, NA, 17.4, 21.2, 26.6, 27.9, NA, 36.3, 40.4) ) # Default behaviour is to silently drop m1 <- lm(y ~ x, data = df) resid(m1) # Use na.action = na.warn to warn m2 <- lm(y ~ x, data = df, na.action = na.warn) resid(m2)df <- tibble::tibble( x = 1:10, y = c(5.1, 9.7, NA, 17.4, 21.2, 26.6, 27.9, NA, 36.3, 40.4) ) # Default behaviour is to silently drop m1 <- lm(y ~ x, data = df) resid(m1) # Use na.action = na.warn to warn m2 <- lm(y ~ x, data = df, na.action = na.warn) resid(m2)
n permutation replicates.A permutation test involves permuting one or more variables in a data set before performing the test, in order to break any existing relationships and simulate the null hypothesis. One can then compare the true statistic to the generated distribution of null statistics.
permute(data, n, ..., .id = ".id") permute_(data, n, columns, .id = ".id")permute(data, n, ..., .id = ".id") permute_(data, n, columns, .id = ".id")
data |
A data frame |
n |
Number of permutations to generate. |
... |
Columns to permute. This supports bare column names or dplyr dplyr::select_helpers |
.id |
Name of variable that gives each model a unique integer id. |
columns |
In |
A data frame with n rows and one column: perm
library(purrr) perms <- permute(mtcars, 100, mpg) models <- map(perms$perm, ~ lm(mpg ~ wt, data = .)) glanced <- map_df(models, broom::glance, .id = "id") # distribution of null permutation statistics hist(glanced$statistic) # confirm these are roughly uniform p-values hist(glanced$p.value) # test against the unpermuted model to get a permutation p-value mod <- lm(mpg ~ wt, mtcars) mean(glanced$statistic > broom::glance(mod)$statistic)library(purrr) perms <- permute(mtcars, 100, mpg) models <- map(perms$perm, ~ lm(mpg ~ wt, data = .)) glanced <- map_df(models, broom::glance, .id = "id") # distribution of null permutation statistics hist(glanced$statistic) # confirm these are roughly uniform p-values hist(glanced$p.value) # test against the unpermuted model to get a permutation p-value mod <- lm(mpg ~ wt, mtcars) mean(glanced$statistic > broom::glance(mod)$statistic)
Often you will resample a dataset hundreds or thousands of times. Storing
the complete resample each time would be very inefficient so this class
instead stores a "pointer" to the original dataset, and a vector of row
indexes. To turn this into a regular data frame, call as.data.frame,
to extract the indices, use as.integer.
resample(data, idx)resample(data, idx)
data |
The data frame |
idx |
A vector of integer indexes indicating which rows have
been selected. These values should lie between 1 and |
Other resampling techniques:
bootstrap(),
resample_bootstrap(),
resample_partition()
resample(mtcars, 1:10) b <- resample_bootstrap(mtcars) b as.integer(b) as.data.frame(b) # Many modelling functions will do the coercion for you, so you can # use a resample object directly in the data argument lm(mpg ~ wt, data = b)resample(mtcars, 1:10) b <- resample_bootstrap(mtcars) b as.integer(b) as.data.frame(b) # Many modelling functions will do the coercion for you, so you can # use a resample object directly in the data argument lm(mpg ~ wt, data = b)
Generate a boostrap replicate
resample_bootstrap(data)resample_bootstrap(data)
data |
A data frame |
Other resampling techniques:
bootstrap(),
resample_partition(),
resample()
coef(lm(mpg ~ wt, data = resample_bootstrap(mtcars))) coef(lm(mpg ~ wt, data = resample_bootstrap(mtcars))) coef(lm(mpg ~ wt, data = resample_bootstrap(mtcars)))coef(lm(mpg ~ wt, data = resample_bootstrap(mtcars))) coef(lm(mpg ~ wt, data = resample_bootstrap(mtcars))) coef(lm(mpg ~ wt, data = resample_bootstrap(mtcars)))
Generate an exclusive partitioning of a data frame
resample_partition(data, p)resample_partition(data, p)
data |
A data frame |
p |
A named numeric vector giving where the value is the probability that an observation will be assigned to that group. |
Other resampling techniques:
bootstrap(),
resample_bootstrap(),
resample()
ex <- resample_partition(mtcars, c(test = 0.3, train = 0.7)) mod <- lm(mpg ~ wt, data = ex$train) rmse(mod, ex$test) rmse(mod, ex$train)ex <- resample_partition(mtcars, c(test = 0.3, train = 0.7)) mod <- lm(mpg ~ wt, data = ex$train) rmse(mod, ex$test) rmse(mod, ex$train)
Create a resampled permutation of a data frame
resample_permutation(data, columns, idx = NULL)resample_permutation(data, columns, idx = NULL)
data |
A data frame |
columns |
Columns to be permuted |
idx |
Indices to permute by. If not given, generates them randomly |
A permutation object; use as.data.frame to convert to a permuted data frame
Generate a sequence over the range of a vector
seq_range(x, n, by, trim = NULL, expand = NULL, pretty = FALSE)seq_range(x, n, by, trim = NULL, expand = NULL, pretty = FALSE)
x |
A numeric vector |
n, by
|
Specify the output sequence either by supplying the
length of the sequence with I recommend that you name these arguments in order to make it clear to the reader. |
trim |
Optionally, trim values off the tails.
|
expand |
Optionally, expand the range by |
pretty |
If |
x <- rcauchy(100) seq_range(x, n = 10) seq_range(x, n = 10, trim = 0.1) seq_range(x, by = 1, trim = 0.1) # Make pretty sequences y <- runif(100) seq_range(y, n = 10) seq_range(y, n = 10, pretty = TRUE) seq_range(y, n = 10, expand = 0.5, pretty = TRUE) seq_range(y, by = 0.1) seq_range(y, by = 0.1, pretty = TRUE)x <- rcauchy(100) seq_range(x, n = 10) seq_range(x, n = 10, trim = 0.1) seq_range(x, by = 1, trim = 0.1) # Make pretty sequences y <- runif(100) seq_range(y, n = 10) seq_range(y, n = 10, pretty = TRUE) seq_range(y, n = 10, expand = 0.5, pretty = TRUE) seq_range(y, by = 0.1) seq_range(y, by = 0.1, pretty = TRUE)
These simple simulated datasets are useful for teaching modelling basics.
sim1 sim2 sim3 sim4sim1 sim2 sim3 sim4
For numeric, integer, and ordered factor vectors, it returns the median.
For factors, characters, and logical vectors, it returns the most
frequent value. If multiple values are tied for most frequent, it returns
them all. NA missing values are always silently dropped.
typical(x, ...)typical(x, ...)
x |
A vector |
... |
Arguments used by methods |
# median of numeric vector typical(rpois(100, lambda = 10)) # most frequent value of character or factor x <- sample(c("a", "b", "c"), 100, prob = c(0.6, 0.2, 0.2), replace = TRUE) typical(x) typical(factor(x)) # if tied, returns them all x <- c("a", "a", "b", "b", "c") typical(x) # median of an ordered factor typical(ordered(c("a", "a", "b", "c", "d")))# median of numeric vector typical(rpois(100, lambda = 10)) # most frequent value of character or factor x <- sample(c("a", "b", "c"), 100, prob = c(0.6, 0.2, 0.2), replace = TRUE) typical(x) typical(factor(x)) # if tied, returns them all x <- c("a", "a", "b", "b", "c") typical(x) # median of an ordered factor typical(ordered(c("a", "a", "b", "c", "d")))