Help for package regrrr

Type:

Package

Title:

Toolkit for Compiling, (Post-Hoc) Testing, and Plotting Regression Results

Version:

0.1.3

Description:

Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).

Depends:

R (≥ 3.5.0)

Maintainer:

Rui K. Yang <rkzyang@gmail.com>

License:

GPL-3

Encoding:

UTF-8

Suggests:

testthat

RoxygenNote:

7.0.2

Imports:

stats, purrr, dplyr, magrittr, tidyr, usdm, scales, robustbase, stringr, MuMIn, ggplot2, lspline

BugReports:

https://github.com/RkzYang/regrrr/issues

NeedsCompilation:

Packaged:

2021-08-13 13:43:49 UTC; ray_mac

Author:

Rui K. Yang [aut, cre], Luyao Peng [aut]

Repository:

CRAN

Date/Publication:

2021-08-13 14:00:05 UTC

regrrr: a toolkit for compiling regression results

Description

Compiling, Testing, Plotting Regression Results

Author(s)

Rui K. Yang rkzyang@gmail.com

Luyao Peng luyaopeng.cn@gmail.com

Add row numbers to regression result data.frame

Description

Add row numbers to regression result data.frame

Usage

add.n.r(df)

Arguments

df

a data.frame of regression result

Add approximate p-value based on t score or z score, when sample size is large

Description

Add approximate p-value based on t score or z score, when sample size is large

Usage

add.pr(df, z.col = 3, p.already = FALSE)

Arguments

df

a data.frame of regression result

z.col

the column number of t score or z score

p.already

whether the regression result already contains p.value

Add significance level marks to the regression result

Description

Add significance level marks to the regression result

Usage

add.sig(df, Pr.col = 5)

Arguments

df

a data.frame of regression result, e.g. summary(a_lm_model)$coefficients

Pr.col

the column number of p.value

quickly check correlation matrix, or the correlation between a particular X and all other vars could be useful for looking for relevant instrument

Description

quickly check correlation matrix, or the correlation between a particular X and all other vars could be useful for looking for relevant instrument

Usage

check_cor(data, var_name_select = NULL, d = 3)

Arguments

data

a data.frame used in regression model

var_name_select

to specify the variable names to be included in the table, default is NULL–all variables are included

d

number of digits retained after the decimal point

Examples

data(mtcars)
check_cor(mtcars)

quickly check the proportion of NAs in each columns of a dataframe

Description

quickly check the proportion of NAs in each columns of a dataframe

Usage

check_na_in(data, true_total = FALSE)

Arguments

data

a data.frame

true_total

FALSE to show the percentage, TRUE to show the true number of missing values

Examples

data(mtcars)
check_na_in(mtcars)

quickly check the vifs in a regression model; for checking multi-collinearity

Description

quickly check the vifs in a regression model; for checking multi-collinearity

Usage

check_vif(data)

Arguments

data

a data.frame used in regression model

Examples

data(mtcars)
model <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
check_vif(data = model$model)

Combine regression results from different models by columns

Description

Combine regression results from different models by columns

Usage

combine_long_tab(
  tbl_1,
  tbl_2,
  tbl_3 = NULL,
  tbl_4 = NULL,
  tbl_5 = NULL,
  tbl_6 = NULL,
  tbl_7 = NULL,
  tbl_8 = NULL,
  tbl_9 = NULL,
  tbl_10 = NULL,
  tbl_11 = NULL,
  tbl_12 = NULL,
  tbl_13 = NULL,
  tbl_14 = NULL,
  tbl_15 = NULL,
  tbl_16 = NULL,
  tbl_17 = NULL,
  tbl_18 = NULL,
  tbl_19 = NULL,
  tbl_20 = NULL
)

Arguments

tbl_1

the 1st data.frame of regression result

tbl_2

the 2nd data.frame of regression result

tbl_3

the 3rd data.frame of regression result

tbl_4

the 4th data.frame of regression result

tbl_5

the 5th data.frame of regression result

tbl_6

the 6th data.frame of regression result

tbl_7

the 7th data.frame of regression result

tbl_8

the 8th data.frame of regression result

tbl_9

the 9th data.frame of regression result

tbl_10

the 10th data.frame of regression result

tbl_11

the 11th data.frame of regression result

tbl_12

the 12th data.frame of regression result

tbl_13

the 13th data.frame of regression result

tbl_14

the 14th data.frame of regression result

tbl_15

the 15th data.frame of regression result

tbl_16

the 16th data.frame of regression result

tbl_17

the 17th data.frame of regression result

tbl_18

the 18th data.frame of regression result

tbl_19

the 19th data.frame of regression result

tbl_20

the 20th data.frame of regression result

Examples

data(mtcars)
m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
m2 <- update(m1, . ~ .+ wt * vs)
summary(m1)
summary(m2)
combine_long_tab(to_long_tab(summary(m1)$coef), 
                 to_long_tab(summary(m2)$coef))

Compare regression models, which is compatible with the reg.table output # updated 9/13/2018 #

Description

Compare regression models, which is compatible with the reg.table output # updated 9/13/2018 #

Usage

compare_models(
  model1,
  model2,
  model3 = NULL,
  model4 = NULL,
  model5 = NULL,
  model6 = NULL,
  model7 = NULL,
  model8 = NULL,
  model9 = NULL,
  model10 = NULL,
  model11 = NULL,
  model12 = NULL,
  model13 = NULL,
  model14 = NULL,
  model15 = NULL,
  model16 = NULL,
  model17 = NULL,
  model18 = NULL,
  model19 = NULL,
  model20 = NULL,
  likelihood.only = FALSE,
  round.digit = 3,
  main.effect.only = NULL,
  intn.effect.only = NULL
)

Arguments

model1

the 1st regression model

model2

the 2nd regression model

model3

the 3rd regression model

model4

the 4th regression model

model5

the 5th regression model

model6

the 6th regression model

model7

the 7th regression model

model8

the 8th regression model

model9

the 9th regression model

model10

the 10th regression model

model11

the 11th regression model

model12

the 12th regression model

model13

the 13th regression model

model14

the 14th regression model

model15

the 15th regression model

model16

the 16th regression model

model17

the 17th regression model

model18

the 18th regression model

model19

the 19th regression model

model20

the 20th regression model

likelihood.only

whether or not to output the likelihood

round.digit

number of decimal places to retain

main.effect.only

specify col number of alternative main-effect models, if any

intn.effect.only

specify col number of alternative moderator models, if any

Examples

data(mtcars)
m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
m2 <- update(m1, . ~ .+ wt * vs)
compare_models(m1, m2)

make the correlation matrix from the data.frame used in regression

Description

make the correlation matrix from the data.frame used in regression

Usage

cor.table(
  data,
  data_to_combine = NULL,
  var_name_select = NULL,
  all.var.names = NULL,
  d = 2
)

Arguments

data

a data.frame used in regression model, e.g. model$model

data_to_combine

another data.frame used for regression model, e.g. when you have similar set of X's but different Y's

var_name_select

optional: to specify the variable names used in regression to be included in the correlation matrix

all.var.names

optional: to rename all variable names, a string vector

d

number of decimal places to retain

Examples

data(mtcars)
model <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
cor.table(data = model$model)

load multiple packages

Description

load multiple packages

Usage

load.pkgs(pkg_name_vec)

Arguments

pkg_name_vec

a string vector of package names

Examples

## Not run: 
load.pkgs(c("dplyr", "car", "purrr"))

## End(Not run)

plotting the marginal effect of X on Y, with or without one or multiple interaction terms

Description

plotting the marginal effect of X on Y, with or without one or multiple interaction terms

Usage

plot_effect(
  reg.coef,
  data,
  model,
  by_color = FALSE,
  x_var.name = NULL,
  y_var.name = NULL,
  moderator.name = NULL,
  min_x = 0.001,
  max_x = 0.999,
  mdrt_quantile_05 = NULL,
  mdrt_quantile_50 = NULL,
  mdrt_quantile_95 = NULL,
  mod.n.sd = 1,
  confidence_interval = FALSE,
  v = NULL,
  CI_Ribbon = FALSE,
  title = NULL,
  xlab = "X_Var.name",
  ylab = "Y_Var.name",
  moderator.lab = "Moderator_name",
  mdrt.low.name = "Low",
  mdrt.mid.name = NULL,
  mdrt.high.name = "High",
  y.high.lim = NULL,
  y.low.lim = NULL,
  spline_labels = c("LHS", "RHS")
)

Arguments

reg.coef

a coefficient matrix of regression result, e.g. summary(lm_model)$coef

data

the data used in regression, a data frame

model

the model object, such as a "lm" object

by_color

plot interactions by colors, otherwise by line types

x_var.name

x name in the regression model, a string

y_var.name

y name in the regression model, a string

moderator.name

moderating variable name in the regression model, a string

min_x

the min of x scale, in percentile of x

max_x

the max of x scale, in percentile of x

mdrt_quantile_05

set the low level of moderator, in percentile

mdrt_quantile_50

set the middle level of moderator, in percentile

mdrt_quantile_95

set the high level of moderator, in percentile

mod.n.sd

set the moderating strength, in the number of s.d. units, which can take negative values

confidence_interval

if TRUE, plot confidence intervals

v

a customized variance-covariance matrix

CI_Ribbon

if TRUE, plot confidence interval ribbons, if FALSE, plot error bars

title

the title of the plot

xlab

label of X

ylab

label of Y

moderator.lab

label of moderator

mdrt.low.name

the label of low-level moderator

mdrt.mid.name

the label of mid-level moderator

mdrt.high.name

the label of high-level moderator

y.high.lim

specify the upper limit of y

y.low.lim

specify the lower limit of y

spline_labels

label of the spline variable; when the main varaible is a linear spline and spline labels are supplied, the moderation effect will be presented by facets.

Examples

## Not run: 
data(mtcars)
m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
plot_effect(reg.coef = summary(m1)$coefficients,
                 data = mtcars, model = m1,
                 x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp",
                 confidence_interval = TRUE,  CI_Ribbon = TRUE,
                 xlab = "Weight", ylab = "MPG", moderator.lab = "Horsepower")

## End(Not run)

#' @examples
## Not run: 
data(mtcars)
m2 <- lm(mpg ~ vs + carb + hp + wt + wt * hp + wt * vs, data = mtcars)
plot_effect(reg.coef = summary(m2)$coefficients,
            data = mtcars, model = m2,
            x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp",
            confidence_interval = TRUE,  CI_Ribbon = FALSE,
            xlab = "Weight", ylab = "MPG", moderator.lab = "Horsepower")

## End(Not run)

#' @examples
## Not run: 
data(mtcars)
m3 <- lm(mpg ~ vs + carb + hp + lspline(wt, knots = 4, marginal = FALSE) * hp, data = mtcars)
plot_effect(reg.coef=summary(m3)$coefficients, 
            data = mtcars, model = m3, x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp",
            xlab="Weight", ylab="MPG", moderator.lab="Horsepower") 

## End(Not run)

## Not run: 
# this shows the function is compatible with ggplot2 customization
library(extrafont)
m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
plot_effect(reg.coef = summary(m1)$coefficients, 
                 data = mtcars, model = m1, 
                 x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp", 
                 confidence_interval = TRUE,  CI_Ribbon = TRUE, 
                 xlab = "Weight", ylab = "MPG", moderator.lab = "Horsepower") + 
ggplot2::theme(text=ggplot2::element_text(family="Times New Roman", size = 16))

## End(Not run)

Scale a vector into the 0-1 scale

Description

Scale a vector into the 0-1 scale

Usage

scale_01(x)

Arguments

x

a vector

testing equality of two coefficients (difference between coefficients of regressors), a Wald test note: if v is not alternatively specified, use car::linearHypothesis(lm_model, "X1 = X2")

Description

testing equality of two coefficients (difference between coefficients of regressors), a Wald test note: if v is not alternatively specified, use car::linearHypothesis(lm_model, "X1 = X2")

Usage

test_coef_equality(model, var1.name, var2.name, v = NULL)

Arguments

model

the model object, such as a "lm" object

var1.name

X1 name in model, a string

var2.name

X2 name in model, a string

v

a customized variance-covariance matrix

data(mtcars) m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars) summary(m1) test_coef_equality(model = m1, var1.name = "carb", var2.name = "hp")

significance of regression slope (the marginal effect) under moderation testing restriction: the sig. of beta_x under the moderation of z1, with or without additional interaction terms (z2, z3, etc.)

Description

significance of regression slope (the marginal effect) under moderation testing restriction: the sig. of beta_x under the moderation of z1, with or without additional interaction terms (z2, z3, etc.)

Usage

test_tilted_slopes(
  reg.coef,
  v = NULL,
  model,
  x_var.name,
  moderator.name,
  mod.n.sd = 1,
  data,
  t.value.col = 3,
  Pr.col = 4
)

Arguments

reg.coef

a data.frame (or matrix) of regression result or a coeftest object, e.g. summary(lm_model)$coef, coeftest(lm_model, cluster.vcov(lm_model, cbind(data$group1, data$group2)))

v

a customized variance-covariance matrix

model

the model object, such as a "lm" object

x_var.name

main independent variable name in model, a string

moderator.name

moderator name in model, a string

mod.n.sd

specify the strength of the moderating effects, in the unit of s.d.s of the moderator, which can take negative values

data

data used for regression

t.value.col

col number of the t-score in reg.coef

Pr.col

col number of the Prob.(>|t|)) in reg.coef

Examples

data(mtcars)
m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
test_tilted_slopes(reg.coef = summary(m1)$coef, model = m1, 
                   x_var.name = "wt", moderator.name = "hp", data = mtcars)

Convert the regression result to the long format: the standard errors are in parentheses and beneath the betas

Description

Convert the regression result to the long format: the standard errors are in parentheses and beneath the betas

Usage

to_long_tab(reg.coef, d = 3, t.value.col = 3, Pr.col = 4)

Arguments

reg.coef

a data.frame (or matrix) of regression result or a coeftest object, e.g. summary(lm_model)$coef, coeftest(lm_model, cluster.vcov(lm_model, cbind(data$group1, data$group2)))

d

number of decimal places to retain

t.value.col

col number of the t-score in the reg.coef data.frame

Pr.col

col number of the Prob.(>|t|)) in the reg.coef data.frame

Examples

data(mtcars)
m1 <- lm(mpg ~ vs + carb + hp + wt + wt * hp , data = mtcars)
to_long_tab(reg.coef = summary(m1)$coef)

regrrr: a toolkit for compiling regression results

Description

Author(s)

See Also

Add row numbers to regression result data.frame

Description

Usage

Arguments

Add approximate p-value based on t score or z score, when sample size is large

Description

Usage

Arguments

Add significance level marks to the regression result

Description

Usage

Arguments

quickly check correlation matrix, or the correlation between a particular X and all other vars could be useful for looking for relevant instrument

Description

Usage

Arguments

Examples

quickly check the proportion of NAs in each columns of a dataframe

Description

Usage

Arguments

Examples

quickly check the vifs in a regression model; for checking multi-collinearity

Description

Usage

Arguments

Examples

Combine regression results from different models by columns

Description

Usage

Arguments

Examples

Compare regression models, which is compatible with the reg.table output # updated 9/13/2018 #

Description

Usage

Arguments

Examples

make the correlation matrix from the data.frame used in regression

Description

Usage

Arguments

Examples

load multiple packages

Description

Usage

Arguments

Examples

plotting the marginal effect of X on Y, with or without one or multiple interaction terms

Description

Usage

Arguments

Examples

Scale a vector into the 0-1 scale

Description

Usage

Arguments

testing equality of two coefficients (difference between coefficients of regressors), a Wald test note: if v is not alternatively specified, use car::linearHypothesis(lm_model, "X1 = X2")

Description

Usage

Arguments

significance of regression slope (the marginal effect) under moderation testing restriction: the sig. of beta_x under the moderation of z1, with or without additional interaction terms (z2, z3, etc.)

Description

Usage

Arguments

Examples

Convert the regression result to the long format: the standard errors are in parentheses and beneath the betas

Description

Usage

Arguments

Examples