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Generate prediction table from model

pred_table.Rd

Generate a table of predicted values and CIs from an asreml or glmmTMB model, for the specified variables asreml or glmmTMB object.

Usage

pred_table(
  mod,
  classify,
  link_fun = "identity",
  tmb_component = "cond",
  tmb_type = "response",
  factor_combine = TRUE,
  trt_col_label = "Treatment"
)

Arguments

mod

The model object to get predictions from.

The value may be:

  • asreml

  • glmmTBM

classify

character vector. Variable for which predictions will be made.

For asreml::asreml() models, it is passed to classify argument of asremlPlus::predictPlus.asreml(). For glmmTMB models it is used in the specs argument of emmeans::emmeans()

character vector. Specifies the transformation function to apply over the predictions. Only applies for asreml::asreml() objects.

The value may be:

  • "identity" (default)

  • "log"

  • "inverse"

  • "sqrt"

  • "logit"

  • "probit"

  • "cloglog"

tmb_component

character vector. Specifies the component of the glmmTMB model from which to get predictions.

The value may be:

  • "cond" (default)

  • "zi"

  • "cmean"

  • "response"

tmb_type

character vector. Specifies the prediction type for glmmTMB models.

  • Only "response" is supported at the moment.

factor_combine

Logical Whether or not to combine the factors in classify into a single column in the output table. If TRUE (default), the factor levels are concatenated and labelled using trt_col_label.

trt_col_label

character vector. Specifies the label for the combined factor column when factor_combine is TRUE. Defaults to "Treatment"

Value

data.frame. Contains the predicted means, standard errors, and confidence intervals for the specified variables. Includes the following columns:

Treatment

The combined factor levels, if factor_combine is TRUE.

Mean

The predicted mean values.

Standard Error

The standard errors of the predicted means.

Lower CL

The lower confidence limits.

Upper CL

The upper confidence limits.

If factor_combine is FALSE, the factors specified in classify remain as separate columns.

Author

Matthew Nguyen, matthew.nguyen@curtin.edu.au

Examples

library(CBADASReml)
library(asreml)
library(glmmTMB)
mod1 <- asreml(
    fixed = yield ~ Variety + Nitrogen + Variety:Nitrogen,
    random = ~ idv(Blocks) + idv(Blocks):idv(Wplots),
    residual = ~ idv(units),
    data = oats
)
# Zero inflated model
mod2 <- glmmTMB(
    count ~ spp * mined + (1|site),
    zi = ~ spp * mined,
    data = Salamanders,
    family = nbinom2
)
#> Warning: Model convergence problem; singular convergence (7). See vignette('troubleshooting'), help('diagnose')
pred_table(mod1, classify = "Variety")
#> Error: Classify must be type character