Summary of Regression Models as HTML Table

2024-05-13

tab_model() is the pendant to plot_model(), however, instead of creating plots, tab_model() creates HTML-tables that will be displayed either in your IDE’s viewer-pane, in a web browser or in a knitr-markdown-document (like this vignette).

HTML is the only output-format, you can’t (directly) create a LaTex or PDF output from tab_model() and related table-functions. However, it is possible to easily export the tables into Microsoft Word or Libre Office Writer.

This vignette shows how to create table from regression models with tab_model(). There’s a dedicated vignette that demonstrate how to change the table layout and appearance with CSS.

Note! Due to the custom CSS, the layout of the table inside a knitr-document differs from the output in the viewer-pane and web browser!

library(sjPlot)
library(sjmisc)
library(sjlabelled)

# sample data
data("efc")
efc <- as_factor(efc, c161sex, c172code)

A simple HTML table from regression results

First, we fit two linear models to demonstrate the tab_model()-function.

m1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)
m2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + e17age, data = efc)

The simplest way of producing the table output is by passing the fitted model as parameter. By default, estimates, confidence intervals (CI) and p-values (p) are reported. As summary, the numbers of observations as well as the R-squared values are shown.

tab_model(m1)
Total score BARTHEL INDEX
Predictors Estimates CI p
(Intercept) 87.15 77.96 – 96.34 <0.001
carer’age -0.21 -0.35 – -0.07 0.004
average number of hours
of care per week
-0.28 -0.32 – -0.24 <0.001
carer’s gender: Female -0.39 -4.49 – 3.71 0.850
carer’s level of
education: intermediate
level of education
1.37 -3.12 – 5.85 0.550
carer’s level of
education: high level of
education
-1.64 -7.22 – 3.93 0.564
Observations 821
R2 / R2 adjusted 0.271 / 0.266

Automatic labelling

As the sjPlot-packages features labelled data, the coefficients in the table are already labelled in this example. The name of the dependent variable(s) is used as main column header for each model. For non-labelled data, the coefficient names are shown.

data(mtcars)
m.mtcars <- lm(mpg ~ cyl + hp + wt, data = mtcars)
tab_model(m.mtcars)
mpg
Predictors Estimates CI p
(Intercept) 38.75 35.09 – 42.41 <0.001
cyl -0.94 -2.07 – 0.19 0.098
hp -0.02 -0.04 – 0.01 0.140
wt -3.17 -4.68 – -1.65 <0.001
Observations 32
R2 / R2 adjusted 0.843 / 0.826

If factors are involved and auto.label = TRUE, “pretty” parameters names are used (see format_parameters().

set.seed(2)
dat <- data.frame(
y = runif(100, 0, 100),
drug = as.factor(sample(c("nonsense", "useful", "placebo"), 100, TRUE)),
group = as.factor(sample(c("control", "treatment"), 100, TRUE))
)

pretty_names <- lm(y ~ drug * group, data = dat)
tab_model(pretty_names)
y
Predictors Estimates CI p
(Intercept) 66.84 52.97 – 80.71 <0.001
drug [placebo] -7.18 -28.25 – 13.89 0.500
drug [useful] -30.95 -53.08 – -8.82 0.007
group [treatment] -21.66 -40.13 – -3.19 0.022
drug [placebo] × group
[treatment]
4.15 -23.68 – 31.98 0.768
drug [useful] × group
[treatment]
30.85 2.38 – 59.33 0.034
Observations 100
R2 / R2 adjusted 0.116 / 0.069

Turn off automatic labelling

To turn off automatic labelling, use auto.label = FALSE, or provide an empty character vector for pred.labels and dv.labels.

tab_model(m1, auto.label = FALSE)
barthtot
Predictors Estimates CI p
(Intercept) 87.15 77.96 – 96.34 <0.001
c160age -0.21 -0.35 – -0.07 0.004
c12hour -0.28 -0.32 – -0.24 <0.001
c161sex2 -0.39 -4.49 – 3.71 0.850
c172code2 1.37 -3.12 – 5.85 0.550
c172code3 -1.64 -7.22 – 3.93 0.564
Observations 821
R2 / R2 adjusted 0.271 / 0.266

Same for models with non-labelled data and factors.

tab_model(pretty_names, auto.label = FALSE)
y
Predictors Estimates CI p
(Intercept) 66.84 52.97 – 80.71 <0.001
drugplacebo -7.18 -28.25 – 13.89 0.500
druguseful -30.95 -53.08 – -8.82 0.007
grouptreatment -21.66 -40.13 – -3.19 0.022
drugplacebo:grouptreatment 4.15 -23.68 – 31.98 0.768
druguseful:grouptreatment 30.85 2.38 – 59.33 0.034
Observations 100
R2 / R2 adjusted 0.116 / 0.069

More than one model

tab_model() can print multiple models at once, which are then printed side-by-side. Identical coefficients are matched in a row.

tab_model(m1, m2)
Total score BARTHEL INDEX Negative impact with 7
items
Predictors Estimates CI p Estimates CI p
(Intercept) 87.15 77.96 – 96.34 <0.001 9.83 7.33 – 12.33 <0.001
carer’age -0.21 -0.35 – -0.07 0.004 0.01 -0.01 – 0.03 0.359
average number of hours
of care per week
-0.28 -0.32 – -0.24 <0.001 0.02 0.01 – 0.02 <0.001
carer’s gender: Female -0.39 -4.49 – 3.71 0.850 0.43 -0.15 – 1.01 0.147
carer’s level of
education: intermediate
level of education
1.37 -3.12 – 5.85 0.550
carer’s level of
education: high level of
education
-1.64 -7.22 – 3.93 0.564
elder’age 0.01 -0.03 – 0.04 0.741
Observations 821 879
R2 / R2 adjusted 0.271 / 0.266 0.067 / 0.063

Generalized linear models

For generalized linear models, the ouput is slightly adapted. Instead of Estimates, the column is named Odds Ratios, Incidence Rate Ratios etc., depending on the model. The coefficients are in this case automatically converted (exponentiated). Furthermore, pseudo R-squared statistics are shown in the summary.

m3 <- glm(
tot_sc_e ~ c160age + c12hour + c161sex + c172code,
data = efc,
)

efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc\$neg_c_7, na.rm = TRUE), 0, 1)
m4 <- glm(
neg_c_7d ~ c161sex + barthtot + c172code,
data = efc,
)

tab_model(m3, m4)
Services for elderly neg c 7 d
Predictors Incidence Rate Ratios CI p Odds Ratios CI p
(Intercept) 0.30 0.21 – 0.45 <0.001 6.54 3.66 – 11.96 <0.001
carer’age 1.01 1.01 – 1.02 <0.001
average number of hours
of care per week
1.00 1.00 – 1.00 <0.001
carer’s gender: Female 1.01 0.87 – 1.19 0.867 1.87 1.31 – 2.69 0.001
carer’s level of
education: intermediate
level of education
1.47 1.21 – 1.79 <0.001 1.23 0.84 – 1.82 0.288
carer’s level of
education: high level of
education
1.90 1.52 – 2.38 <0.001 1.37 0.84 – 2.23 0.204
Total score BARTHEL INDEX 0.97 0.96 – 0.97 <0.001
Observations 840 815
R2 Nagelkerke 0.106 0.191

Untransformed estimates on the linear scale

To plot the estimates on the linear scale, use transform = NULL.

tab_model(m3, m4, transform = NULL, auto.label = FALSE)
tot_sc_e neg_c_7d
Predictors Log-Mean CI p Log-Odds CI p
(Intercept) -1.19 -1.58 – -0.80 <0.001 1.88 1.30 – 2.48 <0.001
c160age 0.01 0.01 – 0.02 <0.001
c12hour 0.00 0.00 – 0.00 <0.001
c161sex2 0.01 -0.15 – 0.18 0.867 0.63 0.27 – 0.99 0.001
c172code2 0.39 0.19 – 0.58 <0.001 0.21 -0.18 – 0.60 0.288
c172code3 0.64 0.42 – 0.87 <0.001 0.31 -0.17 – 0.80 0.204
barthtot -0.03 -0.04 – -0.03 <0.001
Observations 840 815
R2 Nagelkerke 0.106 0.191

More complex models

Other models, like hurdle- or zero-inflated models, also work with tab_model(). In this case, the zero inflation model is indicated in the table. Use show.zeroinf = FALSE to hide this part from the table.

library(pscl)
data("bioChemists")
m5 <- zeroinfl(art ~ fem + mar + kid5 + ment | kid5 + phd + ment, data = bioChemists)

tab_model(m5)
art
Predictors Incidence Rate Ratios CI p
Count Model
(Intercept) 1.83 1.61 – 2.10 <0.001
fem [Women] 0.80 0.72 – 0.90 <0.001
mar [Married] 1.14 1.01 – 1.30 0.041
kid5 0.86 0.78 – 0.94 0.001
ment 1.02 1.01 – 1.02 <0.001
Zero-Inflated Model
(Intercept) 0.45 0.20 – 1.01 0.054
kid5 1.12 0.79 – 1.58 0.531
phd 1.02 0.78 – 1.33 0.881
ment 0.88 0.81 – 0.95 0.002
Observations 915
R2 / R2 adjusted 0.230 / 0.226

You can combine any model in one table.

tab_model(m1, m3, m5, auto.label = FALSE, show.ci = FALSE)
barthtot tot_sc_e art
Predictors Estimates p Incidence Rate Ratios p Incidence Rate Ratios p
(Intercept) 87.15 <0.001 0.30 <0.001
c160age -0.21 0.004 1.01 <0.001
c12hour -0.28 <0.001 1.00 <0.001
c161sex2 -0.39 0.850 1.01 0.867
c172code2 1.37 0.550 1.47 <0.001
c172code3 -1.64 0.564 1.90 <0.001
count_(Intercept) 1.83 <0.001
count_femWomen 0.80 <0.001
count_marMarried 1.14 0.041
count_kid5 0.86 0.001
count_ment 1.02 <0.001
Zero-Inflated Model
zero_(Intercept) 0.45 0.054
zero_kid5 1.12 0.531
zero_phd 1.02 0.881
zero_ment 0.88 0.002
Observations 821 840 915
R2 / R2 adjusted 0.271 / 0.266 0.106 0.230 / 0.226

Show or hide further columns

tab_model() has some argument that allow to show or hide specific columns from the output:

• show.est to show/hide the column with model estimates.
• show.ci to show/hide the column with confidence intervals.
• show.se to show/hide the column with standard errors.
• show.std to show/hide the column with standardized estimates (and their standard errors).
• show.p to show/hide the column with p-values.
• show.stat to show/hide the column with the coefficients’ test statistics.
• show.df for linear mixed models, when p-values are based on degrees of freedom with Kenward-Rogers approximation, these degrees of freedom are shown.

In the following example, standard errors, standardized coefficients and test statistics are also shown.

tab_model(m1, show.se = TRUE, show.std = TRUE, show.stat = TRUE)
Total score BARTHEL INDEX
Predictors Estimates std. Error std. Beta standardized std. Error CI standardized CI Statistic p
(Intercept) 87.15 4.68 -0.01 0.08 77.96 – 96.34 -0.17 – 0.16 18.62 <0.001
carer’age -0.21 0.07 -0.09 0.03 -0.35 – -0.07 -0.16 – -0.03 -2.87 0.004
average number of hours
of care per week
-0.28 0.02 -0.48 0.03 -0.32 – -0.24 -0.54 – -0.42 -14.95 <0.001
carer’s gender: Female -0.39 2.09 -0.01 0.07 -4.49 – 3.71 -0.15 – 0.13 -0.19 0.850
carer’s level of
education: intermediate
level of education
1.37 2.28 0.05 0.08 -3.12 – 5.85 -0.11 – 0.20 0.60 0.550
carer’s level of
education: high level of
education
-1.64 2.84 -0.06 0.10 -7.22 – 3.93 -0.24 – 0.13 -0.58 0.564
Observations 821
R2 / R2 adjusted 0.271 / 0.266

Removing columns

In the following example, default columns are removed.

tab_model(m3, m4, show.ci = FALSE, show.p = FALSE, auto.label = FALSE)
tot_sc_e neg_c_7d
Predictors Incidence Rate Ratios Odds Ratios
(Intercept) 0.30 6.54
c160age 1.01
c12hour 1.00
c161sex2 1.01 1.87
c172code2 1.47 1.23
c172code3 1.90 1.37
barthtot 0.97
Observations 840 815
R2 Nagelkerke 0.106 0.191

Removing and sorting columns

Another way to remove columns, which also allows to reorder the columns, is the col.order-argument. This is a character vector, where each element indicates a column in the output. The value "est", for instance, indicates the estimates, while "std.est" is the column for standardized estimates and so on.

By default, col.order contains all possible columns. All columns that should shown (see previous tables, for example using show.se = TRUE to show standard errors, or show.st = TRUE to show standardized estimates) are then printed by default. Colums that are excluded from col.order are not shown, no matter if the show*-arguments are TRUE or FALSE. So if show.se = TRUE, butcol.order does not contain the element "se", standard errors are not shown. On the other hand, if show.est = FALSE, but col.order does include the element "est", the columns with estimates are not shown.

In summary, col.order can be used to exclude columns from the table and to change the order of colums.

tab_model(
m1, show.se = TRUE, show.std = TRUE, show.stat = TRUE,
col.order = c("p", "stat", "est", "std.se", "se", "std.est")
)
Total score BARTHEL INDEX
Predictors p Statistic Estimates standardized std. Error std. Error std. Beta
(Intercept) <0.001 18.62 87.15 0.08 4.68 -0.01
carer’age 0.004 -2.87 -0.21 0.03 0.07 -0.09
average number of hours
of care per week
<0.001 -14.95 -0.28 0.03 0.02 -0.48
carer’s gender: Female 0.850 -0.19 -0.39 0.07 2.09 -0.01
carer’s level of
education: intermediate
level of education
0.550 0.60 1.37 0.08 2.28 0.05
carer’s level of
education: high level of
education
0.564 -0.58 -1.64 0.10 2.84 -0.06
Observations 821
R2 / R2 adjusted 0.271 / 0.266

Collapsing columns

With collapse.ci and collapse.se, the columns for confidence intervals and standard errors can be collapsed into one column together with the estimates. Sometimes this table layout is required.

tab_model(m1, collapse.ci = TRUE)
Total score BARTHEL INDEX
Predictors Estimates p
(Intercept) 87.15
(77.96 – 96.34)
<0.001
carer’age -0.21
(-0.35 – -0.07)
0.004
average number of hours
of care per week
-0.28
(-0.32 – -0.24)
<0.001
carer’s gender: Female -0.39
(-4.49 – 3.71)
0.850
carer’s level of
education: intermediate
level of education
1.37
(-3.12 – 5.85)
0.550
carer’s level of
education: high level of
education
-1.64
(-7.22 – 3.93)
0.564
Observations 821
R2 / R2 adjusted 0.271 / 0.266

Defining own labels

There are different options to change the labels of the column headers or coefficients, e.g. with:

• pred.labels to change the names of the coefficients in the Predictors column. Note that the length of pred.labels must exactly match the amount of predictors in the Predictor column.
• dv.labels to change the names of the model columns, which are labelled with the variable labels / names from the dependent variables.
• Further more, there are various string.*-arguments, to change the name of column headings.
tab_model(
m1, m2,
pred.labels = c("Intercept", "Age (Carer)", "Hours per Week", "Gender (Carer)",
"Education: middle (Carer)", "Education: high (Carer)",
"Age (Older Person)"),
dv.labels = c("First Model", "M2"),
string.pred = "Coeffcient",
string.ci = "Conf. Int (95%)",
string.p = "P-Value"
)
First Model M2
Coeffcient Estimates Conf. Int (95%) P-Value Estimates Conf. Int (95%) P-Value
Intercept 87.15 77.96 – 96.34 <0.001 9.83 7.33 – 12.33 <0.001
Age (Carer) -0.21 -0.35 – -0.07 0.004 0.01 -0.01 – 0.03 0.359
Hours per Week -0.28 -0.32 – -0.24 <0.001 0.02 0.01 – 0.02 <0.001
Gender (Carer) -0.39 -4.49 – 3.71 0.850 0.43 -0.15 – 1.01 0.147
Education: middle (Carer) 1.37 -3.12 – 5.85 0.550
Education: high (Carer) -1.64 -7.22 – 3.93 0.564
Age (Older Person) 0.01 -0.03 – 0.04 0.741
Observations 821 879
R2 / R2 adjusted 0.271 / 0.266 0.067 / 0.063

Including reference level of categorical predictors

By default, for categorical predictors, the variable names and the categories for regression coefficients are shown in the table output.

library(glmmTMB)
data("Salamanders")
model <- glm(
count ~ spp + Wtemp + mined + cover,
family = poisson(),
data = Salamanders
)

tab_model(model)
count
Predictors Incidence Rate Ratios CI p
(Intercept) 0.22 0.17 – 0.29 <0.001
spp [PR] 0.25 0.16 – 0.38 <0.001
spp [DM] 1.26 0.98 – 1.62 0.074
spp [EC-A] 0.46 0.33 – 0.64 <0.001
spp [EC-L] 1.86 1.48 – 2.36 <0.001
spp [DES-L] 1.97 1.57 – 2.49 <0.001
spp [DF] 1.08 0.83 – 1.41 0.549
Wtemp 1.00 0.93 – 1.08 0.977
mined [no] 9.97 7.91 – 12.69 <0.001
cover 0.79 0.73 – 0.86 <0.001
Observations 644
R2 Nagelkerke 0.758

You can include the reference level for categorical predictors by setting show.reflvl = TRUE.

tab_model(model, show.reflvl = TRUE)
count
Predictors Incidence Rate Ratios CI p
(Intercept) 0.22 0.17 – 0.29 <0.001
Wtemp 1.00 0.93 – 1.08 0.977
cover 0.79 0.73 – 0.86 <0.001
GP Reference
PR 0.25 0.16 – 0.38 <0.001
DM 1.26 0.98 – 1.62 0.074
EC-A 0.46 0.33 – 0.64 <0.001
EC-L 1.86 1.48 – 2.36 <0.001
DES-L 1.97 1.57 – 2.49 <0.001
DF 1.08 0.83 – 1.41 0.549
yes Reference
no 9.97 7.91 – 12.69 <0.001
Observations 644
R2 Nagelkerke 0.758

To show variable names, categories and include the reference level, also set prefix.labels = "varname".

tab_model(model, show.reflvl = TRUE, prefix.labels = "varname")
count
Predictors Incidence Rate Ratios CI p
(Intercept) 0.22 0.17 – 0.29 <0.001
Wtemp 1.00 0.93 – 1.08 0.977
cover 0.79 0.73 – 0.86 <0.001
spp: GP Reference
spp: PR 0.25 0.16 – 0.38 <0.001
spp: DM 1.26 0.98 – 1.62 0.074
spp: EC-A 0.46 0.33 – 0.64 <0.001
spp: EC-L 1.86 1.48 – 2.36 <0.001
spp: DES-L 1.97 1.57 – 2.49 <0.001
spp: DF 1.08 0.83 – 1.41 0.549
mined: yes Reference
mined: no 9.97 7.91 – 12.69 <0.001
Observations 644
R2 Nagelkerke 0.758

Style of p-values

You can change the style of how p-values are displayed with the argument p.style. With p.style = "stars", the p-values are indicated as * in the table.

tab_model(m1, m2, p.style = "stars")
Total score BARTHEL INDEX Negative impact with 7
items
Predictors Estimates CI Estimates CI
(Intercept) 87.15 *** 77.96 – 96.34 9.83 *** 7.33 – 12.33
carer’age -0.21 ** -0.35 – -0.07 0.01 -0.01 – 0.03
average number of hours
of care per week
-0.28 *** -0.32 – -0.24 0.02 *** 0.01 – 0.02
carer’s gender: Female -0.39 -4.49 – 3.71 0.43 -0.15 – 1.01
carer’s level of
education: intermediate
level of education
1.37 -3.12 – 5.85
carer’s level of
education: high level of
education
-1.64 -7.22 – 3.93
elder’age 0.01 -0.03 – 0.04
Observations 821 879
R2 / R2 adjusted 0.271 / 0.266 0.067 / 0.063
• p<0.05   ** p<0.01   *** p<0.001

Another option would be scientific notation, using p.style = "scientific", which also can be combined with digits.p.

tab_model(m1, m2, p.style = "scientific", digits.p = 2)
Total score BARTHEL INDEX Negative impact with 7
items
Predictors Estimates CI p Estimates CI p
(Intercept) 87.15 77.96 – 96.34 9.33e-65 9.83 7.33 – 12.33 3.11e-14
carer’age -0.21 -0.35 – -0.07 4.18e-03 0.01 -0.01 – 0.03 3.59e-01
average number of hours
of care per week
-0.28 -0.32 – -0.24 7.77e-45 0.02 0.01 – 0.02 2.69e-11
carer’s gender: Female -0.39 -4.49 – 3.71 8.50e-01 0.43 -0.15 – 1.01 1.47e-01
carer’s level of
education: intermediate
level of education
1.37 -3.12 – 5.85 5.50e-01
carer’s level of
education: high level of
education
-1.64 -7.22 – 3.93 5.64e-01
elder’age 0.01 -0.03 – 0.04 7.41e-01
Observations 821 879
R2 / R2 adjusted 0.271 / 0.266 0.067 / 0.063

Automatic matching for named vectors

Another way to easily assign labels are named vectors. In this case, it doesn’t matter if pred.labels has more labels than coefficients in the model(s), or in which order the labels are passed to tab_model(). The only requirement is that the labels’ names equal the coefficients names as they appear in the summary()-output.

# example, coefficients are "c161sex2" or "c172code3"
summary(m1)
#>
#> Call:
#> lm(formula = barthtot ~ c160age + c12hour + c161sex + c172code,
#>     data = efc)
#>
#> Residuals:
#>     Min      1Q  Median      3Q     Max
#> -75.144 -14.944   4.401  18.661  72.393
#>
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 87.14994    4.68009  18.621  < 2e-16 ***
#> c160age     -0.20716    0.07211  -2.873  0.00418 **
#> c12hour     -0.27883    0.01865 -14.950  < 2e-16 ***
#> c161sex2    -0.39402    2.08893  -0.189  0.85044
#> c172code2    1.36596    2.28440   0.598  0.55004
#> c172code3   -1.64045    2.84037  -0.578  0.56373
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 25.35 on 815 degrees of freedom
#>   (87 observations deleted due to missingness)
#> Multiple R-squared:  0.2708, Adjusted R-squared:  0.2664
#> F-statistic: 60.54 on 5 and 815 DF,  p-value: < 2.2e-16

pl <- c(
(Intercept) = "Intercept",
e17age = "Age (Older Person)",
c160age = "Age (Carer)",
c12hour = "Hours per Week",
barthtot = "Barthel-Index",
c161sex2 = "Gender (Carer)",
c172code2 = "Education: middle (Carer)",
c172code3 = "Education: high (Carer)",
a_non_used_label = "We don't care"
)

tab_model(
m1, m2, m3, m4,
pred.labels = pl,
dv.labels = c("Model1", "Model2", "Model3", "Model4"),
show.ci = FALSE,
show.p = FALSE,
transform = NULL
)
Model1 Model2 Model3 Model4
Predictors Estimates Estimates Log-Mean Log-Odds
Intercept 87.15 9.83 -1.19 1.88
Age (Carer) -0.21 0.01 0.01
Hours per Week -0.28 0.02 0.00
Gender (Carer) -0.39 0.43 0.01 0.63
Education: middle (Carer) 1.37 0.39 0.21
Education: high (Carer) -1.64 0.64 0.31
Age (Older Person) 0.01
Barthel-Index -0.03
Observations 821 879 840 815
R2 / R2 adjusted 0.271 / 0.266 0.067 / 0.063 0.106 0.191

Keep or remove coefficients from the table

Using the terms- or rm.terms-argument allows us to explicitly show or remove specific coefficients from the table output.

tab_model(m1, terms = c("c160age", "c12hour"))
Total score BARTHEL INDEX
Predictors Estimates CI p
carer’age -0.21 -0.35 – -0.07 0.004
average number of hours
of care per week
-0.28 -0.32 – -0.24 <0.001
Observations 821
R2 / R2 adjusted 0.271 / 0.266

Note that the names of terms to keep or remove should match the coefficients names. For categorical predictors, one example would be:

tab_model(m1, rm.terms = c("c172code2", "c161sex2"))
Total score BARTHEL INDEX
Predictors Estimates CI p
(Intercept) 87.15 77.96 – 96.34 <0.001
carer’age -0.21 -0.35 – -0.07 0.004
average number of hours
of care per week
-0.28 -0.32 – -0.24 <0.001
carer’s level of
education: high level of
education
-1.64 -7.22 – 3.93 0.564
Observations 821
R2 / R2 adjusted 0.271 / 0.266