!! Compute data for a qq-plot

Source: R/qq_data.R

Compute data necessary to plot a quantile comparison plot (qq-plot).

qq_data(
  y,
  data = NULL,
  distribution = "norm",
  ...,
  line = c("quartiles", "robust", "int=0,slope=1", "0,1", "none"),
  envelope = 0.95,
  method = c("mle-normal", "trimmed-normal", "moment-normal", "any"),
  labels = NULL,
  groups = NULL,
  sep = " | "
)

# S3 method for qqdata
coef(object, ...)

# S3 method for qqdata
plot(x, ..., use_colors = FALSE, scales = "free")

Arguments

y

(formula|numeric|character)
Either a formula, a numeric vector or a name of a column in data.

  • If y is a formula (e.g. variable ~ factor), left-hand side provides the name of a variable to be tested. In the right-hand side there are names of factor variables to be used to create subsets. If the left-hand side is empty (e.g. ~ factor), right-hand side is treated as variable name to test.
data

(data frame|NULL)
Either a data frame that contains the variables mentioned in y or NULL (if the variables are in the function's environment).
...

Parameters to be passed to function, selected in distribution. In print method, further parameters to function print.
line

(string) A parameter, that controls how reference line is drawn. Options:

  • "0,1" or "int=0,slope=1" to plot a line with intercept = 0 and slope = 1;

  • "quartiles" to pass a line through the quartile-pairs;

  • "robust" for a robust-regression line; the latter uses the rlm function from the MASS package;

  • option "none" is not implemented yet.
envelope

(numeric | FALSE) confidence level for point-wise confidence envelope.
method

(string: "trimmed-normal", "normal", "any"). A method, that controls how estimates of parameters for distribution are computed. Options:

  • "mle-normal" (default) all data values are used to estimate parameters of theoretical normal distribution using method of maximum likelihood;

  • "trimmed-normal" 10\ data values are trimmed before parameters of theoretical normal distribution are estimated using method of moments;

  • "moment-normal" all data values are used to estimate parameters of theoretical normal distribution using method of moments;

  • "any" either parameters are provided manually by user or default parameters are used (if any).

        Options `"mle-normal"`, `"trimmed-normal"` and
    `"moment-normal"` are applicable only if
    `distribution = "norm"`.
    Otherwise `"any"` is selected automatically.
groups

(NULL|factor|character)
An alternative way to provide groups. If y is a numeric vector, groups must be a factor (or NULL). If y is a sting, groups must also be a string (or NULL).
sep

(not used yet).
object

A qqdata object.
x

A qqdata object.
use_colors

(logical) use colors for multiple groups
scales

("free"|"free_x"|"free_y"|"fixed") a parmeter to be passed to ggplot2::facet_wrap().

Value

An object, which inherits from classes qqdata and data.frame. The object contains information, needed to plot a qqplot with reference line and its confidence intervals. These variables are contained:

  • x – x axis values;

  • y – y axis values for points of qq plot;

  • labels – labels for each point;

  • ref_y – y axis values for reference line

  • ref_ci_upper and ref_ci_lower – y axis values for upper and lower pointwise confidence interval of a reference line.

Details

Function qq_data is inspired by qqPlot() in package car (writen by J. Fox).

See also

car::qqPlot() in car package, stats::qqplot() in stats package.

Examples


library(biostat)
data(chickwts, package = "datasets")

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Input as formula + data:

my_qq_df <- qq_data(~weight, data = chickwts)
head(my_qq_df)
#>            x   y labels     ref_y ref_ci_upper ref_ci_lower
#> 7   70.98569 108      7  47.42369     135.0090    -40.16157
#> 8  103.86168 124      8  84.83439     143.0135     26.65531
#> 3  121.05847 136      3 104.40320     153.1069     55.69954
#> 10 133.26529 140     10 118.29376     161.8545     74.73305
#> 14 142.93372 141     14 129.29579     169.5062     89.08535
#> 9  151.04785 143      9 138.52913     176.3377    100.72059
coef(my_qq_df)
#>            intercept    slope refline_type plot_envelope conf
#> qq_refline -33.35333 1.137934    quartiles          TRUE 0.95

# Column ".group" is added if applied by group:

my_qq_df <- qq_data(weight ~ feed, data = chickwts)
head(my_qq_df)
#>   .group        x   y labels    ref_y ref_ci_upper ref_ci_lower
#> 1 casein 216.7557 216     68 203.9755     291.9501     116.0009
#> 2 casein 252.6175 222     69 244.2674     307.2708     181.2639
#> 3 casein 273.4770 260     63 267.7038     323.2246     212.1830
#> 4 casein 289.7446 283     70 285.9810     337.9149     234.0472
#> 5 casein 303.9262 318     65 301.9146     351.9821     251.8470
#> 6 casein 317.1284 332     71 316.7477     365.9956     267.4998
coef(my_qq_df)
#>                   .group intercept     slope refline_type plot_envelope conf
#> qq_refline...1    casein -39.55714 1.1235348    quartiles          TRUE 0.95
#> qq_refline...2 horsebean  29.42192 0.7940267    quartiles          TRUE 0.95
#> qq_refline...3   linseed -40.70818 1.1820946    quartiles          TRUE 0.95
#> qq_refline...4  meatmeal  50.88326 0.8445614    quartiles          TRUE 0.95
#> qq_refline...5   soybean  16.85751 0.8989116    quartiles          TRUE 0.95
#> qq_refline...6 sunflower 183.09510 0.4359916    quartiles          TRUE 0.95

qq_plot(weight ~ feed, data = chickwts)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Input as variable name + data:

my_qq_df <- qq_data("weight", data = chickwts)
head(my_qq_df)
#>            x   y labels     ref_y ref_ci_upper ref_ci_lower
#> 7   70.98569 108      7  47.42369     135.0090    -40.16157
#> 8  103.86168 124      8  84.83439     143.0135     26.65531
#> 3  121.05847 136      3 104.40320     153.1069     55.69954
#> 10 133.26529 140     10 118.29376     161.8545     74.73305
#> 14 142.93372 141     14 129.29579     169.5062     89.08535
#> 9  151.04785 143      9 138.52913     176.3377    100.72059
coef(my_qq_df)
#>            intercept    slope refline_type plot_envelope conf
#> qq_refline -33.35333 1.137934    quartiles          TRUE 0.95

my_qq_df <- qq_data("weight", groups = "feed", data = chickwts)
head(my_qq_df)
#>   .group        x   y labels    ref_y ref_ci_upper ref_ci_lower
#> 1 casein 216.7557 216     68 203.9755     291.9501     116.0009
#> 2 casein 252.6175 222     69 244.2674     307.2708     181.2639
#> 3 casein 273.4770 260     63 267.7038     323.2246     212.1830
#> 4 casein 289.7446 283     70 285.9810     337.9149     234.0472
#> 5 casein 303.9262 318     65 301.9146     351.9821     251.8470
#> 6 casein 317.1284 332     71 316.7477     365.9956     267.4998
coef(my_qq_df)
#>                   .group intercept     slope refline_type plot_envelope conf
#> qq_refline...1    casein -39.55714 1.1235348    quartiles          TRUE 0.95
#> qq_refline...2 horsebean  29.42192 0.7940267    quartiles          TRUE 0.95
#> qq_refline...3   linseed -40.70818 1.1820946    quartiles          TRUE 0.95
#> qq_refline...4  meatmeal  50.88326 0.8445614    quartiles          TRUE 0.95
#> qq_refline...5   soybean  16.85751 0.8989116    quartiles          TRUE 0.95
#> qq_refline...6 sunflower 183.09510 0.4359916    quartiles          TRUE 0.95

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Input as vector

my_qq_df <- qq_data(chickwts$weight)
head(my_qq_df)
#>            x   y labels     ref_y ref_ci_upper ref_ci_lower
#> 7   70.98569 108      7  47.42369     135.0090    -40.16157
#> 8  103.86168 124      8  84.83439     143.0135     26.65531
#> 3  121.05847 136      3 104.40320     153.1069     55.69954
#> 10 133.26529 140     10 118.29376     161.8545     74.73305
#> 14 142.93372 141     14 129.29579     169.5062     89.08535
#> 9  151.04785 143      9 138.52913     176.3377    100.72059
coef(my_qq_df)
#>            intercept    slope refline_type plot_envelope conf
#> qq_refline -33.35333 1.137934    quartiles          TRUE 0.95

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Input as vector, several groups.
# Column ".group" is added

my_qq_df <- qq_data(chickwts$weight, groups = chickwts$feed)
head(my_qq_df)
#>   .group        x   y labels    ref_y ref_ci_upper ref_ci_lower
#> 1 casein 216.7557 216     68 203.9755     291.9501     116.0009
#> 2 casein 252.6175 222     69 244.2674     307.2708     181.2639
#> 3 casein 273.4770 260     63 267.7038     323.2246     212.1830
#> 4 casein 289.7446 283     70 285.9810     337.9149     234.0472
#> 5 casein 303.9262 318     65 301.9146     351.9821     251.8470
#> 6 casein 317.1284 332     71 316.7477     365.9956     267.4998
coef(my_qq_df)
#>                   .group intercept     slope refline_type plot_envelope conf
#> qq_refline...1    casein -39.55714 1.1235348    quartiles          TRUE 0.95
#> qq_refline...2 horsebean  29.42192 0.7940267    quartiles          TRUE 0.95
#> qq_refline...3   linseed -40.70818 1.1820946    quartiles          TRUE 0.95
#> qq_refline...4  meatmeal  50.88326 0.8445614    quartiles          TRUE 0.95
#> qq_refline...5   soybean  16.85751 0.8989116    quartiles          TRUE 0.95
#> qq_refline...6 sunflower 183.09510 0.4359916    quartiles          TRUE 0.95
library(biostat)
data(chickwts, package = "datasets")

# Input as formula + data:

my_qq_df <- qq_data(weight ~ feed, data = chickwts)
plot(my_qq_df)

my_qq_df2 <- qq_data(weight ~ feed, data = chickwts, method = "moment-normal")
plot(my_qq_df2)

my_qq_df3 <- qq_data(weight ~ feed, data = chickwts, method = "any")
plot(my_qq_df3)


# The same x and y scale limits for all plots
plot(my_qq_df, scales = "fixed")

# Plot in color
plot(my_qq_df, use_colors = TRUE)

# Plot a qq-plot (with no grouping)
qq_single <- qq_data(~weight, data = chickwts)
plot(qq_single)

class(qq_single)
#> [1] "qqdata"     "data.frame"


# More than one grouping variable
data(CO2, package = "datasets")

qq_co2 <- qq_data(uptake ~ Type + Treatment, data = CO2)
plot(qq_co2)

qq_co2_B <- qq_data(uptake ~ Type + Treatment, data = CO2, line = "robust")
plot(qq_co2_B)