Predict method for the logistic function

Predict method for the logistic function

# S3 method for logistic
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  conf_lev = 0.95,
  se = TRUE,
  interval = "confidence",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object	Return value from logistic
pred_data	Provide the dataframe to generate predictions (e.g., titanic). The dataset must contain all columns used in the estimation
pred_cmd	Generate predictions using a command. For example, `pclass = levels(pclass)` would produce predictions for the different levels of factor `pclass`. To add another variable, create a vector of prediction strings, (e.g., c('pclass = levels(pclass)', 'age = seq(0,100,20)')
conf_lev	Confidence level used to estimate confidence intervals (.95 is the default)
se	Logical that indicates if prediction standard errors should be calculated (default = FALSE)
interval	Type of interval calculation ("confidence" or "none"). Set to "none" if se is FALSE
dec	Number of decimals to show
envir	Environment to extract data from
...	further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/logistic.html for an example in Radiant

See also

logistic to generate the result

summary.logistic to summarize results

plot.logistic to plot results

plot.model.predict to plot prediction output

Examples

result <- logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes")
  predict(result, pred_cmd = "pclass = levels(pclass)")
#> Logistic regression (GLM)
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes in survived 
#> Explanatory variables: pclass, sex 
#> Interval             : confidence 
#> Prediction command   : pclass = levels(pclass) 
#> 
#>   sex pclass Prediction  2.5% 97.5%
#>  male    1st      0.408 0.340 0.480
#>  male    2nd      0.220 0.170 0.280
#>  male    3rd      0.111 0.086 0.142
logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>%
  predict(pred_cmd = "sex = c('male','female')")
#> Logistic regression (GLM)
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes in survived 
#> Explanatory variables: pclass, sex 
#> Interval             : confidence 
#> Prediction command   : sex = c('male', 'female') 
#> 
#>  pclass    sex Prediction  2.5% 97.5%
#>     3rd   male      0.111 0.086 0.142
#>     3rd female      0.608 0.540 0.673
logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>%
  predict(pred_data = titanic)
#> Logistic regression (GLM)
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes in survived 
#> Explanatory variables: pclass, sex 
#> Interval             : confidence 
#> Prediction dataset   : titanic 
#> Rows shown           : 10 of 1,043 
#> 
#>  pclass    sex Prediction  2.5% 97.5%
#>     1st female      0.896 0.856 0.926
#>     1st   male      0.408 0.340 0.480
#>     1st female      0.896 0.856 0.926
#>     1st   male      0.408 0.340 0.480
#>     1st female      0.896 0.856 0.926
#>     1st   male      0.408 0.340 0.480
#>     1st female      0.896 0.856 0.926
#>     1st   male      0.408 0.340 0.480
#>     1st female      0.896 0.856 0.926
#>     1st   male      0.408 0.340 0.480