packDAMipd (version 0.2.0)

use_generalised_linear_mixed_model: Function for generalised linear mixed model

Description

Function for generalised linear mixed model

Usage

use_generalised_linear_mixed_model(
  param_to_be_estimated,
  dataset,
  fix_eff,
  fix_eff_interact_vars,
  random_intercept_vars,
  nested_intercept_vars_pairs,
  cross_intercept_vars_pairs,
  uncorrel_slope_intercept_pairs,
  random_slope_intercept_pairs,
  family,
  link,
  package_mixed_model
)

Arguments

param_to_be_estimated

column name of dependent variable

dataset

a dataframe

fix_eff

names of variables as fixed effect predictors

fix_eff_interact_vars,

those of the fixed effect predictors that show interaction

random_intercept_vars,

names of variables for random intercept

nested_intercept_vars_pairs,

those of the random intercept variables with nested effect

cross_intercept_vars_pairs,

those of the random intercept variables with crossed effect

uncorrel_slope_intercept_pairs,

variables with no correlated intercepts

random_slope_intercept_pairs,

random slopes intercept pairs - this is a list of paired variables

family,

family of distributions for the response variable

link,

link function for the variances

package_mixed_model

package to be used for mixed model

Value

result regression result with plot if success and -1, if failure

Examples

# NOT RUN {
datafile <- system.file("extdata", "culcita_data.csv",
package = "packDAMipd")
dataset <- read.csv(datafile)
results1 = use_generalised_linear_mixed_model("predation",
dataset = datafile,fix_eff = c("ttt"), family = "binomial",
fix_eff_interact_vars = NULL, random_intercept_vars = c("block"),
nested_intercept_vars_pairs = NULL, cross_intercept_vars_pairs = NULL,
uncorrel_slope_intercept_pairs = NULL, random_slope_intercept_pairs = NULL,
 link = NA, package_mixed_model = NA)
 
# }