Augment accepts a model object and a dataset and adds information about each observation in the dataset. Most commonly, this includes predicted values in the .fitted column, residuals in the .resid column, and standard errors for the fitted values in a .se.fit column. New columns always begin with a . prefix to avoid overwriting columns in the original dataset.

Users may pass data to augment via either the data argument or the newdata argument. If the user passes data to the data argument, it must be exactly the data that was used to fit the model object. Pass datasets to newdata to augment data that was not used during model fitting. This still requires that all columns used to fit the model are present.

Augment will often behavior different depending on whether data or newdata is specified. This is because there is often information associated with training observations (such as influences or related) measures that is not meaningfully defined for new observations.

For convenience, many augment methods provide default data arguments, so that augment(fit) will return the augmented training data. In these cases augment tries to reconstruct the original data based on the model object, with some varying degrees of success.

The augmented dataset is always returned as a tibble::tibble with the same number of rows as the passed dataset. This means that the passed data must be coercible to a tibble. At this time, tibbles do not support matrix-columns. This means you should not specify a matrix of covariates in a model formula during the original model fitting process, and that splines::ns(), stats::poly() and survival::Surv() objects are not supported in input data. If you encounter errors, try explicitly passing a tibble, or fitting the original model on data in a tibble.

We are in the process of defining behaviors for models fit with various na.action arguments, but make no guarantees about behavior when data is missing at this time.

# S3 method for poLCA
augment(x, data = NULL, ...)

Arguments

x

A poLCA object returned from poLCA::poLCA().

data

A data.frame() or tibble::tibble() containing the original data that was used to produce the object x. Defaults to stats::model.frame(x) so that augment(my_fit) returns the augmented original data. Do not pass new data to the data argument. Augment will report information such as influence and cooks distance for data passed to the data argument. These measures are only defined for the original training data.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Details

If the data argument is given, those columns are included in the output (only rows for which predictions could be made). Otherwise, the y element of the poLCA object, which contains the manifest variables used to fit the model, are used, along with any covariates, if present, in x.

Note that while the probability of all the classes (not just the predicted modal class) can be found in the posterior element, these are not included in the augmented output.

See also

Value

A tibble::tibble() with columns:

.class

The predicted class. Needs custom info. (TODO -- make this a factor?)

.probability

Posterior probability of predicted class If the `data` argument is given, those columns are included in the output (only rows for which predictions could be made). Otherwise, the `y` element of the poLCA object, which contains the manifest variables used to fit the model, are used, along with any covariates, if present, in `x`. Note that while the probability of all the classes (not just the predicted modal class) can be found in the `posterior` element, these are not included in the augmented output.

Examples

library(poLCA)
#> Loading required package: scatterplot3d
#> Loading required package: MASS
#> #> Attaching package: ‘MASS’
#> The following object is masked from ‘package:dplyr’: #> #> select
library(dplyr) data(values) f <- cbind(A, B, C, D)~1 M1 <- poLCA(f, values, nclass = 2, verbose = FALSE) M1
#> Conditional item response (column) probabilities, #> by outcome variable, for each class (row) #> #> $A #> Pr(1) Pr(2) #> class 1: 0.2864 0.7136 #> class 2: 0.0068 0.9932 #> #> $B #> Pr(1) Pr(2) #> class 1: 0.6704 0.3296 #> class 2: 0.0602 0.9398 #> #> $C #> Pr(1) Pr(2) #> class 1: 0.6460 0.3540 #> class 2: 0.0735 0.9265 #> #> $D #> Pr(1) Pr(2) #> class 1: 0.8676 0.1324 #> class 2: 0.2309 0.7691 #> #> Estimated class population shares #> 0.7208 0.2792 #> #> Predicted class memberships (by modal posterior prob.) #> 0.6713 0.3287 #> #> ========================================================= #> Fit for 2 latent classes: #> ========================================================= #> number of observations: 216 #> number of estimated parameters: 9 #> residual degrees of freedom: 6 #> maximum log-likelihood: -504.4677 #> #> AIC(2): 1026.935 #> BIC(2): 1057.313 #> G^2(2): 2.719922 (Likelihood ratio/deviance statistic) #> X^2(2): 2.719764 (Chi-square goodness of fit) #>
tidy(M1)
#> # A tibble: 16 x 5 #> variable class outcome estimate std.error #> <chr> <int> <dbl> <dbl> <dbl> #> 1 A 1 1 0.286 0.0393 #> 2 A 2 1 0.00681 0.0254 #> 3 A 1 2 0.714 0.0393 #> 4 A 2 2 0.993 0.0254 #> 5 B 1 1 0.670 0.0489 #> 6 B 2 1 0.0602 0.0649 #> 7 B 1 2 0.330 0.0489 #> 8 B 2 2 0.940 0.0649 #> 9 C 1 1 0.646 0.0482 #> 10 C 2 1 0.0735 0.0642 #> 11 C 1 2 0.354 0.0482 #> 12 C 2 2 0.927 0.0642 #> 13 D 1 1 0.868 0.0379 #> 14 D 2 1 0.231 0.0929 #> 15 D 1 2 0.132 0.0379 #> 16 D 2 2 0.769 0.0929
#> # A tibble: 216 x 7 #> A B C D X.Intercept. .class .probability #> * <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> #> 1 2 2 2 2 1 2 0.959 #> 2 2 2 2 2 1 2 0.959 #> 3 2 2 2 2 1 2 0.959 #> 4 2 2 2 2 1 2 0.959 #> 5 2 2 2 2 1 2 0.959 #> 6 2 2 2 2 1 2 0.959 #> 7 2 2 2 2 1 2 0.959 #> 8 2 2 2 2 1 2 0.959 #> 9 2 2 2 2 1 2 0.959 #> 10 2 2 2 2 1 2 0.959 #> # ... with 206 more rows
glance(M1)
#> # A tibble: 1 x 7 #> logLik AIC BIC g.squared chi.squared df df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 -504. 1027. 1057. 2.72 2.72 9 6
library(ggplot2) ggplot(tidy(M1), aes(factor(class), estimate, fill = factor(outcome))) + geom_bar(stat = "identity", width = 1) + facet_wrap(~ variable)
## Three-class model with a single covariate. data(election) f2a <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG, MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~PARTY nes2a <- poLCA(f2a, election, nclass = 3, nrep = 5, verbose = FALSE) td <- tidy(nes2a) td
#> # A tibble: 144 x 5 #> variable class outcome estimate std.error #> <chr> <int> <fct> <dbl> <dbl> #> 1 MORALG 1 1 Extremely well 0.622 0.0309 #> 2 MORALG 2 1 Extremely well 0.137 0.0182 #> 3 MORALG 3 1 Extremely well 0.108 0.0175 #> 4 MORALG 1 2 Quite well 0.335 0.0293 #> 5 MORALG 2 2 Quite well 0.668 0.0247 #> 6 MORALG 3 2 Quite well 0.383 0.0274 #> 7 MORALG 1 3 Not too well 0.0172 0.00841 #> 8 MORALG 2 3 Not too well 0.180 0.0208 #> 9 MORALG 3 3 Not too well 0.304 0.0253 #> 10 MORALG 1 4 Not well at all 0.0258 0.0124 #> # ... with 134 more rows
# show ggplot(td, aes(outcome, estimate, color = factor(class), group = class)) + geom_line() + facet_wrap(~ variable, nrow = 2) + theme(axis.text.x = element_text(angle = 90, hjust = 1))
au <- augment(nes2a) au
#> # A tibble: 1,300 x 16 #> MORALG CARESG KNOWG LEADG DISHONG INTELG MORALB CARESB KNOWB LEADB DISHONB #> * <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> #> 1 3 Not… 1 Ext… 2 Qu… 2 Qu… 3 Not … 2 Qui… 1 Ext… 1 Ext… 2 Qu… 2 Qu… 4 Not … #> 2 1 Ext… 2 Qui… 2 Qu… 1 Ex… 3 Not … 2 Qui… 2 Qui… 2 Qui… 2 Qu… 3 No… 3 Not … #> 3 2 Qui… 2 Qui… 2 Qu… 2 Qu… 2 Quit… 2 Qui… 2 Qui… 3 Not… 2 Qu… 2 Qu… 3 Not … #> 4 2 Qui… 4 Not… 2 Qu… 3 No… 2 Quit… 2 Qui… 1 Ext… 1 Ext… 2 Qu… 2 Qu… 3 Not … #> 5 2 Qui… 2 Qui… 2 Qu… 2 Qu… 3 Not … 2 Qui… 3 Not… 4 Not… 4 No… 4 No… 3 Not … #> 6 2 Qui… 2 Qui… 2 Qu… 3 No… 4 Not … 2 Qui… 2 Qui… 3 Not… 2 Qu… 2 Qu… 3 Not … #> 7 1 Ext… 1 Ext… 1 Ex… 1 Ex… 4 Not … 1 Ext… 2 Qui… 4 Not… 2 Qu… 3 No… 3 Not … #> 8 2 Qui… 2 Qui… 2 Qu… 2 Qu… 3 Not … 2 Qui… 3 Not… 2 Qui… 2 Qu… 2 Qu… 3 Not … #> 9 2 Qui… 2 Qui… 2 Qu… 2 Qu… 3 Not … 2 Qui… 2 Qui… 2 Qui… 2 Qu… 3 No… 2 Quit… #> 10 2 Qui… 3 Not… 2 Qu… 2 Qu… 3 Not … 2 Qui… 2 Qui… 4 Not… 2 Qu… 4 No… 2 Quit… #> # ... with 1,290 more rows, and 5 more variables: INTELB <fct>, #> # X.Intercept. <dbl>, PARTY <dbl>, .class <int>, .probability <dbl>
au %>% count(.class)
#> # A tibble: 3 x 2 #> .class n #> <int> <int> #> 1 1 360 #> 2 2 496 #> 3 3 444
# if the original data is provided, it leads to NAs in new columns # for rows that weren't predicted au2 <- augment(nes2a, data = election) au2
#> # A tibble: 1,785 x 20 #> MORALG CARESG KNOWG LEADG DISHONG INTELG MORALB CARESB KNOWB LEADB DISHONB #> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> #> 1 3 Not… 1 Ext… 2 Qu… 2 Qu… 3 Not … 2 Qui… 1 Ext… 1 Ext… 2 Qu… 2 Qu… 4 Not … #> 2 4 Not… 3 Not… 4 No… 3 No… 2 Quit… 2 Qui… <NA> <NA> 2 Qu… 3 No… <NA> #> 3 1 Ext… 2 Qui… 2 Qu… 1 Ex… 3 Not … 2 Qui… 2 Qui… 2 Qui… 2 Qu… 3 No… 3 Not … #> 4 2 Qui… 2 Qui… 2 Qu… 2 Qu… 2 Quit… 2 Qui… 2 Qui… 3 Not… 2 Qu… 2 Qu… 3 Not … #> 5 2 Qui… 4 Not… 2 Qu… 3 No… 2 Quit… 2 Qui… 1 Ext… 1 Ext… 2 Qu… 2 Qu… 3 Not … #> 6 2 Qui… 3 Not… 3 No… 2 Qu… 2 Quit… 2 Qui… 2 Qui… <NA> 3 No… 2 Qu… 2 Quit… #> 7 2 Qui… <NA> 2 Qu… 2 Qu… 4 Not … 2 Qui… <NA> 3 Not… 2 Qu… 2 Qu… 4 Not … #> 8 2 Qui… 2 Qui… 2 Qu… 2 Qu… 3 Not … 2 Qui… 3 Not… 4 Not… 4 No… 4 No… 3 Not … #> 9 2 Qui… 2 Qui… 2 Qu… 3 No… 4 Not … 2 Qui… 2 Qui… 3 Not… 2 Qu… 2 Qu… 3 Not … #> 10 1 Ext… 1 Ext… 1 Ex… 1 Ex… 4 Not … 1 Ext… 2 Qui… 4 Not… 2 Qu… 3 No… 3 Not … #> # ... with 1,775 more rows, and 9 more variables: INTELB <fct>, VOTE3 <dbl>, #> # AGE <dbl>, EDUC <dbl>, GENDER <dbl>, PARTY <dbl>, .class <int>, #> # .probability <dbl>, .rownames <chr>
dim(au2)
#> [1] 1785 20