Description Usage Arguments Details Value Examples
Fit multilevel models online on a data set
1 2 3 4 5 | sema_fit_df(formula, data_frame = data.frame(), intercept = FALSE,
print_every = NA, store_every = NA, start_resid_var = 1,
start_random_var = 1, start_fixed_coef = 1:5, start_cor = 0.15,
update = NULL, train = NULL, threshold = 1e-04, max_iter = 800,
prior_n = 0, prior_j = 0)
|
formula |
A symbolic representation of the model, formula is used
similar to lme4's |
data_frame |
A data frame consisting of the variables mentioned in the formula. |
intercept |
This indicates whether there is a column in data frame with 1's. |
print_every |
Do you want the results printed to the consule? The default is NA, meaning no printing, if a number is privided the function prints a summary of the model every 'print_every' data points. |
store_every |
Do you want to store results during the data stream? The default is NA, i.e., no results are stored, if a number is privided the function stores the fixed effects, random effects variance and residual variance in seperate data frames every 'store_every' data points. |
start_resid_var |
This is optional if the user wants to provide a start value of the residual variance, default start value is 1. |
start_random_var |
This is optional if the user wants to provide a start values of the variance of the random effects covariates, default start value is 1. NOTE, if start values are provided make sure that the length of the vector of start values matches the number of random effects. |
start_fixed_coef |
This is optional if the user wants to provide start values of the fixed effects, default is set to NULL such that sema_fit_one can create the vector of start values matching the number of fixed effects. NOTE, if start values are provided make sure that the length of the vector of start values matches the number of fixed effects. |
start_cor |
This is a starting value for the correlations between the random effects. |
update |
The default is NULL, when an integer is provided
|
train |
The default value is |
threshold |
In case of a training set, this thresholds determines when the EM algorithm should terminate. When the parameter estimates change less than this threshold, EM algorithm terminates. |
max_iter |
In case of a training set, you can fix the number of iterations of the EM algorithm. |
prior_n |
If starting values are provided, prior_n determines the weight of the starting value of the residual variance, default is 0. |
prior_j |
If starting values are provided, prior_j determins the weight of the starting value of the variance of the random effects and the fixed effects, default is 0. |
This function fits the multilevel models online, or row-by-row
on a data set. Similar to sema_fit_set
and
sema_fit_one
the algorithm updates the model parameters a
data point at a time. However, instead of these two functions, this
function fits the multilevel model on a data set and it uses
formula
.
A list with updated global parameters (model),
a list with lists of all units parameters and contributions (unit),
if store_every is a number 3 data frames fixed_coef_df
,
random_var_df
, resid_var_df
.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## First we create a dataset, consisting of 2500 observations from 20
## units. The fixed effects have the coefficients 1, 2, 3, 4, and 5. The
## variance of the random effects equals 1, 4, and 9. Lastly the
## residual variance equals 4:
test_data <- build_dataset(n = 1500,
j = 200,
fixed_coef = 1:5,
random_coef_sd = 1:3,
resid_sd = 2)
## fit a multilevel model:
m1 <- sema_fit_df(formula = y ~ 1 + V3 + V4 + V5 + V6 + (1 + V4 + V5 | id),
data_frame = test_data, intercept = TRUE)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.