The model_parameters() function (also accessible via the shortcut parameters()) allows you to extract the parameters and their characteristics from various models in a consistent way. It can be considered as a lightweight alternative to broom::tidy(), with some notable differences:

  • The names of the returned data frame are specific to their content. For instance, the column containing the statistic is named following the statistic name, i.e., t, z, etc., instead of a generic name such as statistic (however, you can get standardized (generic) column names using standardize_names()).

  • It is able to compute or extract indices not available by default, such as p-values, CIs, etc.

  • It includes feature engineering capabilities, including parameters bootstrapping.

Correlations and t-tests

Frequentist

cor.test(iris$Sepal.Length, iris$Sepal.Width) %>%
  parameters()
#> Pearson's product-moment correlation
#> 
#> Parameter1        |       Parameter2 |     r |        95% CI | t(148) |     p
#> -----------------------------------------------------------------------------
#> iris$Sepal.Length | iris$Sepal.Width | -0.12 | [-0.27, 0.04] |  -1.44 | 0.152
t.test(mpg ~ vs, data = mtcars) %>%
  parameters()
#> Welch Two Sample t-test
#> 
#> Parameter | Group | vs = 0 | vs = 1 | Difference |          95% CI | t(22.72) |      p
#> --------------------------------------------------------------------------------------
#> mpg       |    vs |  16.62 |  24.56 |       7.94 | [-11.46, -4.42] |    -4.67 | < .001

Bayesian

library(BayesFactor)

BayesFactor::correlationBF(iris$Sepal.Length, iris$Sepal.Width) %>%
  parameters()
#> Bayesian correlation analysis
#> 
#> Parameter | Median |        89% CI |     pd | % in ROPE |         Prior |    BF
#> -------------------------------------------------------------------------------
#> rho       |  -0.11 | [-0.23, 0.02] | 92.25% |    18.98% | Beta (3 +- 3) | 0.509
BayesFactor::ttestBF(formula = mpg ~ vs, data = mtcars) %>%
  parameters()
#> Bayesian t-test
#> 
#> Parameter  | Median |         89% CI |     pd | % in ROPE |              Prior |     BF
#> ---------------------------------------------------------------------------------------
#> Difference |   7.31 | [ 4.50, 10.12] | 99.98% |        0% | Cauchy (0 +- 0.71) | 529.27
#> Cohen's D  |  -1.57 | [-2.21, -0.84] | 99.98% |        0% |                    |

ANOVAs

Indices of effect size for ANOVAs, such as partial and non-partial versions of eta_squared(), epsilon_sqared() or omega_squared() are powered by the effectsize-package. However, parameters uses these function to compute such indices for parameters summaries, including confidence intervals

Simple

aov(Sepal.Length ~ Species, data = iris) %>%
  parameters(
    omega_squared = "partial",
    eta_squared = "partial",
    epsilon_squared = "partial"
  )
#> Parameter | Sum_Squares |  df | Mean_Square |      F |      p | Omega2 | Eta2 | Epsilon2
#> ----------------------------------------------------------------------------------------
#> Species   |       63.21 |   2 |       31.61 | 119.26 | < .001 |   0.61 | 0.62 |     0.61
#> Residuals |       38.96 | 147 |        0.27 |        |        |        |      |

Let’s complicate things further with an interaction term:

aov(Sepal.Length ~ Species * Sepal.Width, data = iris) %>%
  parameters(
    omega_squared = "partial",
    eta_squared = "partial",
    ci = .8
  )
#> Parameter           | Sum_Squares |  df | Mean_Square |      F |      p | Omega2 (partial) | Omega2 80% CI | Eta2 (partial) |  Eta2 80% CI
#> ------------------------------------------------------------------------------------------------------------------------------------------
#> Species             |       63.21 |   2 |       31.61 | 163.44 | < .001 |             0.68 |  [0.63, 0.72] |           0.69 | [0.64, 0.73]
#> Sepal.Width         |       10.95 |   1 |       10.95 |  56.64 | < .001 |             0.27 |  [0.19, 0.34] |           0.28 | [0.21, 0.36]
#> Species:Sepal.Width |        0.16 |   2 |        0.08 |   0.41 | 0.667  |        -7.98e-03 |  [0.00, 0.00] |       5.61e-03 | [0.00, 0.02]
#> Residuals           |       27.85 | 144 |        0.19 |        |        |                  |               |                |

Repeated measures

parameters() (resp. its alias model_parameters()) also works on repeated measures ANOVAs, whether computed from aov() or from a mixed model.

aov(mpg ~ am + Error(gear), data = mtcars) %>%
  parameters()
#> # gear
#> 
#> Parameter | Sum_Squares | df | Mean_Square
#> ------------------------------------------
#> am        |      259.75 |  1 |      259.75
#> 
#> # Within
#> 
#> Parameter | Sum_Squares | df | Mean_Square |    F |     p
#> ---------------------------------------------------------
#> am        |      145.45 |  1 |      145.45 | 5.85 | 0.022
#> Residuals |      720.85 | 29 |       24.86 |      |

Regressions (GLMs, Mixed Models, GAMs, …)

parameters() (resp. its alias model_parameters()) was mainly built with regression models in mind. It works for many types of models and packages, including mixed models and Bayesian models.

GLMs

glm(vs ~ poly(mpg, 2) + cyl, data = mtcars, family = binomial()) %>%
  parameters()
#> Parameter        | Log-Odds |   SE |          95% CI |     z |     p
#> --------------------------------------------------------------------
#> (Intercept)      |    13.51 | 7.20 | [  2.56, 33.42] |  1.88 | 0.060
#> mpg [1st degree] |    -6.64 | 8.99 | [-27.81, 11.13] | -0.74 | 0.461
#> mpg [2nd degree] |     1.16 | 3.59 | [ -7.91,  8.56] |  0.32 | 0.746
#> cyl              |    -2.28 | 1.18 | [ -5.58, -0.51] | -1.92 | 0.055
# show Odds Ratios and Wald-method for degrees of freedom
glm(vs ~ poly(mpg, 2) + cyl, data = mtcars, family = binomial()) %>%
  parameters(exponentiate = TRUE, df_method = "wald")
#> Parameter        | Odds Ratio |       SE |           95% CI |     z |     p
#> ---------------------------------------------------------------------------
#> (Intercept)      |   7.38e+05 | 5.31e+06 | [0.55, 9.87e+11] |  1.88 | 0.060
#> mpg [1st degree] |   1.31e-03 |     0.01 | [0.00, 59497.56] | -0.74 | 0.461
#> mpg [2nd degree] |       3.20 |    11.49 | [0.00,  3637.30] |  0.32 | 0.746
#> cyl              |       0.10 |     0.12 | [0.01,     1.05] | -1.92 | 0.055
# show Odds Ratios and include model summary
glm(vs ~ poly(mpg, 2) + cyl, data = mtcars, family = binomial()) %>%
  parameters(exponentiate = TRUE, summary = TRUE)
#> Parameter        | Odds Ratio |       SE |            95% CI |     z |     p
#> ----------------------------------------------------------------------------
#> (Intercept)      |   7.38e+05 | 5.31e+06 | [12.95, 3.26e+14] |  1.88 | 0.060
#> mpg [1st degree] |   1.31e-03 |     0.01 | [ 0.00, 68459.83] | -0.74 | 0.461
#> mpg [2nd degree] |       3.20 |    11.49 | [ 0.00,  5212.21] |  0.32 | 0.746
#> cyl              |       0.10 |     0.12 | [ 0.00,     0.60] | -1.92 | 0.055
#> 
#> Model: vs ~ poly(mpg, 2) + cyl (32 Observations)
#> Residual standard deviation: 0.788 (df = 28)
#> Tjur's R2: 0.670

Mixed Models

library(lme4)

lmer(Sepal.Width ~ Petal.Length + (1 | Species), data = iris) %>%
  parameters()
#> # Fixed Effects
#> 
#> Parameter    | Coefficient |   SE |       95% CI | t(146) |      p
#> ------------------------------------------------------------------
#> (Intercept)  |        2.00 | 0.56 | [0.90, 3.10] |   3.56 | < .001
#> Petal.Length |        0.28 | 0.06 | [0.17, 0.40] |   4.75 | < .001
#> 
#> # Random Effects
#> 
#> Parameter               | Coefficient
#> -------------------------------------
#> SD (Intercept: Species) |        0.89
#> SD (Residual)           |        0.56

Mixed Models, including Random Effects Variances

lmer(Sepal.Width ~ Petal.Length + (1 | Species), data = iris) %>%
  parameters(effects = "all")
#> # Fixed Effects
#> 
#> Parameter    | Coefficient |   SE |       95% CI | t(146) |      p
#> ------------------------------------------------------------------
#> (Intercept)  |        2.00 | 0.56 | [0.90, 3.10] |   3.56 | < .001
#> Petal.Length |        0.28 | 0.06 | [0.17, 0.40] |   4.75 | < .001
#> 
#> # Random Effects
#> 
#> Parameter               | Coefficient
#> -------------------------------------
#> SD (Intercept: Species) |        0.89
#> SD (Residual)           |        0.56

Mixed Model with Zero-Inflation Model

library(GLMMadaptive)
library(glmmTMB)
data("Salamanders")
model <- mixed_model(
  count ~ spp + mined,
  random = ~ 1 | site,
  zi_fixed = ~ spp + mined,
  family = zi.negative.binomial(),
  data = Salamanders
)
parameters(model)
#> # Fixed Effects (Count Model)
#> 
#> Parameter   | Log-Mean |   SE |        95% CI |     z |      p
#> --------------------------------------------------------------
#> (Intercept) |    -0.63 | 0.40 | [-1.42, 0.16] | -1.56 | 0.118 
#> spp [PR]    |    -0.99 | 0.70 | [-2.35, 0.38] | -1.41 | 0.157 
#> spp [DM]    |     0.17 | 0.24 | [-0.29, 0.63] |  0.72 | 0.469 
#> spp [EC-A]  |    -0.39 | 0.35 | [-1.07, 0.29] | -1.13 | 0.258 
#> spp [EC-L]  |     0.49 | 0.24 | [ 0.02, 0.96] |  2.03 | 0.043 
#> spp [DES-L] |     0.59 | 0.23 | [ 0.14, 1.04] |  2.57 | 0.010 
#> spp [DF]    |    -0.11 | 0.24 | [-0.59, 0.37] | -0.46 | 0.642 
#> mined [no]  |     1.45 | 0.37 | [ 0.73, 2.17] |  3.95 | < .001
#> 
#> # Fixed Effects (Zero-Inflated Model)
#> 
#> Parameter   | Log-Odds |   SE |         95% CI |     z |      p
#> ---------------------------------------------------------------
#> (Intercept) |     0.90 | 0.64 | [-0.35,  2.15] |  1.41 | 0.159 
#> spp [PR]    |     1.12 | 1.50 | [-1.82,  4.06] |  0.74 | 0.456 
#> spp [DM]    |    -0.95 | 0.82 | [-2.56,  0.65] | -1.17 | 0.244 
#> spp [EC-A]  |     1.04 | 0.72 | [-0.38,  2.46] |  1.44 | 0.150 
#> spp [EC-L]  |    -0.58 | 0.74 | [-2.03,  0.88] | -0.77 | 0.439 
#> spp [DES-L] |    -0.91 | 0.78 | [-2.43,  0.61] | -1.18 | 0.239 
#> spp [DF]    |    -2.63 | 2.37 | [-7.27,  2.02] | -1.11 | 0.268 
#> mined [no]  |    -2.56 | 0.63 | [-3.80, -1.32] | -4.06 | < .001
#> 
#> # Random Effects Variances
#> 
#> Parameter            | Coefficient
#> ----------------------------------
#> SD (Intercept: site) |        0.39
#> SD (Residual)        |        1.27
#> 
#> # Random Effects (Zero-Inflated Model)
#> 
#> Parameter     | Coefficient
#> ---------------------------
#> SD (Residual) |        1.27

Mixed Models with Dispersion Model

library(glmmTMB)

sim1 <- function(nfac = 40, nt = 100, facsd = 0.1, tsd = 0.15, mu = 0, residsd = 1) {
  dat <- expand.grid(fac = factor(letters[1:nfac]), t = 1:nt)
  n <- nrow(dat)
  dat$REfac <- rnorm(nfac, sd = facsd)[dat$fac]
  dat$REt <- rnorm(nt, sd = tsd)[dat$t]
  dat$x <- rnorm(n, mean = mu, sd = residsd) + dat$REfac + dat$REt
  dat
}

set.seed(101)
d1 <- sim1(mu = 100, residsd = 10)
d2 <- sim1(mu = 200, residsd = 5)
d1$sd <- "ten"
d2$sd <- "five"
dat <- rbind(d1, d2)
model <- glmmTMB(x ~ sd + (1 | t), dispformula = ~sd, data = dat)

parameters(model)
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |            95% CI |       z |      p
#> -----------------------------------------------------------------------
#> (Intercept) |      200.03 | 0.10 | [ 199.84, 200.22] | 2056.35 | < .001
#> sd [ten]    |      -99.71 | 0.22 | [-100.14, -99.29] | -458.39 | < .001
#> 
#> # Dispersion
#> 
#> Parameter   | Coefficient |   SE |       95% CI |      z |      p
#> -----------------------------------------------------------------
#> (Intercept) |        3.20 | 0.03 | [3.15, 3.26] | 115.48 | < .001
#> sd [ten]    |        1.39 | 0.04 | [1.31, 1.46] |  35.35 | < .001
#> 
#> # Random Effects Variances
#> 
#> Parameter         | Coefficient
#> -------------------------------
#> SD (Intercept: t) |    5.56e-04
#> SD (Residual)     |        1.61

Bayesian Models

model_parameters() also works with Bayesian models from the rstanarm package:

library(rstanarm)

# if you are unfamiliar with the `refresh` argument here, it just avoids
# printing few messages to the console
stan_glm(mpg ~ wt * cyl, data = mtcars, refresh = 0) %>%
  parameters()
#> # Fixed effects
#> 
#> Parameter   | Median |          89% CI |     pd | % in ROPE |  Rhat |     ESS |                   Prior
#> -------------------------------------------------------------------------------------------------------
#> (Intercept) |  52.34 | [ 42.50, 61.77] |   100% |        0% | 1.002 | 1154.00 | Normal (20.09 +- 15.07)
#> wt          |  -7.90 | [-11.76, -4.44] | 99.98% |     0.05% | 1.002 | 1250.00 |  Normal (0.00 +- 15.40)
#> cyl         |  -3.51 | [ -5.24, -2.00] |   100% |     0.07% | 1.002 | 1267.00 |   Normal (0.00 +- 8.44)
#> wt:cyl      |   0.70 | [  0.18,  1.21] | 98.55% |    38.00% | 1.002 | 1184.00 |   Normal (0.00 +- 1.36)

Additionally, it also works for models from the brms package.

For more complex models, specific model components can be printed using the arguments effects and component arguments.

library(brms)
data(fish)
set.seed(123)

# fitting a model using `brms`
model <- brm(
  bf(
    count ~ persons + child + camper + (1 | persons),
    zi ~ child + camper + (1 | persons)
  ),
  data = fish,
  family = zero_inflated_poisson(),
  refresh = 0
)

parameters(model, component = "conditional")
#> # Fixed effects
#> 
#> Parameter   | Median |         89% CI |     pd | % in ROPE |  Rhat |     ESS
#> ----------------------------------------------------------------------------
#> (Intercept) |  -0.86 | [-1.34, -0.45] | 99.58% |     0.83% | 1.004 |  957.00
#> persons     |   0.84 | [ 0.71,  1.00] |   100% |        0% | 1.005 |  778.00
#> child       |  -1.15 | [-1.30, -1.01] |   100% |        0% | 1.001 | 2688.00
#> camper1     |   0.73 | [ 0.59,  0.89] |   100% |        0% | 1.001 | 3045.00

parameters(model, effects = "all", component = "all")
#> # Fixed effects (conditional)
#> 
#> Parameter   | Median |         89% CI |     pd | % in ROPE |  Rhat |     ESS
#> ----------------------------------------------------------------------------
#> (Intercept) |  -0.86 | [-1.34, -0.45] | 99.58% |     0.83% | 1.004 |  957.00
#> persons     |   0.84 | [ 0.71,  1.00] |   100% |        0% | 1.005 |  778.00
#> child       |  -1.15 | [-1.30, -1.01] |   100% |        0% | 1.001 | 2688.00
#> camper1     |   0.73 | [ 0.59,  0.89] |   100% |        0% | 1.001 | 3045.00
#> 
#> # Fixed effects (zero-inflated)
#> 
#> Parameter   | Median |         89% CI |     pd | % in ROPE |  Rhat |     ESS
#> ----------------------------------------------------------------------------
#> (Intercept) |  -0.67 | [-1.75,  0.47] | 84.35% |     6.83% | 1.003 | 1160.00
#> child       |   1.86 | [ 1.33,  2.38] |   100% |        0% | 1.000 | 2574.00
#> camper1     |  -0.83 | [-1.39, -0.21] | 98.62% |     1.75% | 1.000 | 2777.00
#> 
#> # Random effects (conditional) SD/Cor: persons
#> 
#> Parameter   | Median |       89% CI |   pd | % in ROPE |  Rhat |    ESS
#> -----------------------------------------------------------------------
#> (Intercept) |   0.11 | [0.00, 0.35] | 100% |    46.42% | 1.003 | 753.00
#> 
#> # Random effects (zero-inflated) SD/Cor: persons
#> 
#> Parameter   | Median |       89% CI |   pd | % in ROPE |  Rhat |     ESS
#> ------------------------------------------------------------------------
#> (Intercept) |   1.32 | [0.46, 2.47] | 100% |        0% | 1.003 | 1183.00

To include information about the random effect parameters (group levels), set group_level = TRUE:

parameters(model, effects = "all", component = "conditional", group_level = TRUE)
#> # Fixed effects
#> 
#> Parameter   | Median |         89% CI |     pd | % in ROPE |  Rhat |     ESS
#> ----------------------------------------------------------------------------
#> (Intercept) |  -0.86 | [-1.34, -0.45] | 99.58% |     0.83% | 1.004 |  957.00
#> persons     |   0.84 | [ 0.71,  1.00] |   100% |        0% | 1.005 |  778.00
#> child       |  -1.15 | [-1.30, -1.01] |   100% |        0% | 1.001 | 2688.00
#> camper1     |   0.73 | [ 0.59,  0.89] |   100% |        0% | 1.001 | 3045.00
#> 
#> # Random effects Intercept: persons
#> 
#> Parameter |    Median |        89% CI |     pd | % in ROPE |  Rhat |     ESS
#> ----------------------------------------------------------------------------
#> persons.1 | -5.23e-03 | [-0.26, 0.25] | 54.77% |    65.95% | 1.003 | 1036.00
#> persons.2 |      0.03 | [-0.14, 0.27] | 65.70% |    66.42% | 1.004 |  827.00
#> persons.3 |     -0.01 | [-0.22, 0.13] | 59.13% |    73.28% | 1.004 |  807.00
#> persons.4 |  2.93e-03 | [-0.26, 0.22] | 52.98% |    66.80% | 1.004 |  594.00
#> 
#> # Random effects SD/Cor: persons
#> 
#> Parameter   | Median |       89% CI |   pd | % in ROPE |  Rhat |    ESS
#> -----------------------------------------------------------------------
#> (Intercept) |   0.11 | [0.00, 0.35] | 100% |    46.42% | 1.003 | 753.00

Structural Models (PCA, EFA, CFA, SEM…)

The parameters package extends the support to structural models.

Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA)

library(psych)

psych::pca(mtcars, nfactors = 3) %>%
  parameters()
#> # Rotated loadings from Principal Component Analysis (varimax-rotation)
#> 
#> Variable |  RC2  |  RC3  |  RC1  | Complexity | Uniqueness
#> ----------------------------------------------------------
#> mpg      | 0.66  | -0.41 | -0.54 |    2.63    |    0.10   
#> cyl      | -0.62 | 0.67  | 0.34  |    2.49    |    0.05   
#> disp     | -0.72 | 0.52  | 0.35  |    2.33    |    0.10   
#> hp       | -0.30 | 0.64  | 0.63  |    2.40    |    0.10   
#> drat     | 0.85  | -0.26 | -0.05 |    1.19    |    0.21   
#> wt       | -0.78 | 0.21  | 0.51  |    1.90    |    0.08   
#> qsec     | -0.18 | -0.91 | -0.28 |    1.28    |    0.06   
#> vs       | 0.28  | -0.86 | -0.23 |    1.36    |    0.12   
#> am       | 0.92  | 0.14  | -0.11 |    1.08    |    0.12   
#> gear     | 0.91  | -0.02 | 0.26  |    1.16    |    0.10   
#> carb     | 0.11  | 0.44  | 0.85  |    1.53    |    0.07   
#> 
#> The 3 principal components (varimax rotation) accounted for 89.87% of the total variance of the original data (RC2 = 41.43%, RC3 = 29.06%, RC1 = 19.39%).

We will avoid displaying a graph while carrying out factor analysis:

library(FactoMineR)

FactoMineR::FAMD(iris, ncp = 3, graph = FALSE) %>%
  parameters()
#> # Loadings from Factor Analysis (no rotation)
#> 
#> Variable     | Dim.1 |  Dim.2   |  Dim.3   | Complexity
#> -------------------------------------------------------
#> Sepal.Length | 0.75  |   0.07   |   0.10   |    1.05   
#> Sepal.Width  | 0.23  |   0.51   |   0.23   |    1.86   
#> Petal.Length | 0.98  | 1.32e-03 | 1.99e-03 |    1.00   
#> Petal.Width  | 0.94  |   0.01   | 2.82e-05 |    1.00   
#> Species      | 0.96  |   0.75   |   0.26   |    2.05   
#> 
#> The 3 latent factors accounted for 96.73% of the total variance of the original data (Dim.1 = 64.50%, Dim.2 = 22.37%, Dim.3 = 9.86%).

Confirmatory Factor Analysis (CFA) and Structural Equation Models (SEM)

Frequentist

library(lavaan)

model <- lavaan::cfa(" visual  =~ x1 + x2 + x3
                       textual =~ x4 + x5 + x6
                       speed   =~ x7 + x8 + x9 ",
  data = HolzingerSwineford1939
)

model_parameters(model)
#> # Loading
#> 
#> Link          | Coefficient |   SE |       95% CI |     z |      p
#> ------------------------------------------------------------------
#> visual =~ x1  |        1.00 | 0.00 | [1.00, 1.00] |       | < .001
#> visual =~ x2  |        0.55 | 0.10 | [0.36, 0.75] |  5.55 | < .001
#> visual =~ x3  |        0.73 | 0.11 | [0.52, 0.94] |  6.68 | < .001
#> textual =~ x4 |        1.00 | 0.00 | [1.00, 1.00] |       | < .001
#> textual =~ x5 |        1.11 | 0.07 | [0.98, 1.24] | 17.01 | < .001
#> textual =~ x6 |        0.93 | 0.06 | [0.82, 1.03] | 16.70 | < .001
#> speed =~ x7   |        1.00 | 0.00 | [1.00, 1.00] |       | < .001
#> speed =~ x8   |        1.18 | 0.16 | [0.86, 1.50] |  7.15 | < .001
#> speed =~ x9   |        1.08 | 0.15 | [0.79, 1.38] |  7.15 | < .001
#> 
#> # Correlation
#> 
#> Link              | Coefficient |   SE |       95% CI |    z |      p
#> ---------------------------------------------------------------------
#> visual ~~ textual |        0.41 | 0.07 | [0.26, 0.55] | 5.55 | < .001
#> visual ~~ speed   |        0.26 | 0.06 | [0.15, 0.37] | 4.66 | < .001
#> textual ~~ speed  |        0.17 | 0.05 | [0.08, 0.27] | 3.52 | < .001

Bayesian

blavaan to be done.

Meta-Analysis

parameters() also works for rma-objects from the metafor package.

library(metafor)

mydat <- data.frame(
  effectsize = c(-0.393, 0.675, 0.282, -1.398),
  standarderror = c(0.317, 0.317, 0.13, 0.36)
)

rma(yi = effectsize, sei = standarderror, method = "REML", data = mydat) %>%
  model_parameters()
#> Meta-analysis using 'metafor'
#> 
#> Parameter | Coefficient |   SE |         95% CI |     z |      p | Weight
#> -------------------------------------------------------------------------
#> Study 1   |       -0.39 | 0.32 | [-1.01,  0.23] | -1.24 | 0.215  |   9.95
#> Study 2   |        0.68 | 0.32 | [ 0.05,  1.30] |  2.13 | 0.033  |   9.95
#> Study 3   |        0.28 | 0.13 | [ 0.03,  0.54] |  2.17 | 0.030  |  59.17
#> Study 4   |       -1.40 | 0.36 | [-2.10, -0.69] | -3.88 | < .001 |   7.72
#> Overall   |       -0.18 | 0.44 | [-1.05,  0.68] | -0.42 | 0.676  |

Plotting Model Parameters

There is a plot()-method implemented in the see-package. Several examples are shown in this vignette.