# predict.glmmPQL

0th

Percentile

##### Predict Method for glmmPQL Fits

Obtains predictions from a fitted generalized linear model with random effects.

Keywords
models
##### Usage
# S3 method for glmmPQL
predict(object, newdata = NULL, type = c("link", "response"),
level, na.action = na.pass, ...)
##### Arguments
object
a fitted object of class inheriting from "glmmPQL".
newdata
optionally, a data frame in which to look for variables with which to predict.
type
the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities.
level
an optional integer vector giving the level(s) of grouping to be used in obtaining the predictions. Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions. Defaults to the highest or innermost level of grouping.
na.action
function determining what should be done with missing values in newdata. The default is to predict NA.
further arguments passed to or from other methods.
##### Value

If level is a single integer, a vector otherwise a data frame.

glmmPQL, predict.lme.
library(MASS) fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family = binomial, data = bacteria) predict(fit, bacteria, level = 0, type="response") predict(fit, bacteria, level = 1, type="response")
{"ft_lang_label":"__label__en","ft_lang_prob":0.699256,"math_prob":0.87229437,"size":1577,"snap":"2020-34-2020-40","text_gpt3_token_len":384,"char_repetition_ratio":0.12841704,"word_repetition_ratio":0.015873017,"special_character_ratio":0.23272036,"punctuation_ratio":0.14440434,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97311413,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-10-01T16:26:31Z\",\"WARC-Record-ID\":\"<urn:uuid:92b2123c-8e91-4519-8000-1f8ad2328960>\",\"Content-Length\":\"15491\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:7983e72d-34b5-4e20-86fe-440958e09480>\",\"WARC-Concurrent-To\":\"<urn:uuid:ea040d23-0d73-46e9-83db-8212bf378a7f>\",\"WARC-IP-Address\":\"54.88.87.244\",\"WARC-Target-URI\":\"https://www.rdocumentation.org/packages/MASS/versions/7.3-47/topics/predict.glmmPQL\",\"WARC-Payload-Digest\":\"sha1:IVPOJHXBQRMGYSN6FINW6PTVQ2CLDBGU\",\"WARC-Block-Digest\":\"sha1:P2JXPVSMCD54DS6WROWFVMV6VN426SMX\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-40/CC-MAIN-2020-40_segments_1600402131777.95_warc_CC-MAIN-20201001143636-20201001173636-00688.warc.gz\"}"}