Ошибка : Преобразование 'rpy2py' не определено для объектов типа

Я использую библиотеку python R для реализации модели ERGM для набора данных. Я следую приведенному ниже подходу

Когда я реализую этот код, я получаю ошибку Conversion 'rpy2py' not defined for objects of type error

Я попытался реализовать следующий метод

Но когда я подхожу к нему, он выдает ошибку, связанную с fit_summary$se, говоря, что аргументы равны 4,0

Maximum Pseudolikelihood Results:

                     Estimate Std. Error MCMC % z value Pr(>|z|)    
edges              -11.728186   1.263969      0  -9.279   <1e-04 ***
triangle             2.088686   0.108218      0  19.301   <1e-04 ***
mutual               8.638105   0.817793      0  10.563   <1e-04 ***
nodecov.Attendance  -0.034097   0.007857      0  -4.339   <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Warning:  The standard errors are based on naive pseudolikelihood and are suspect. Set control.ergm$MPLE.covariance.method='Godambe' for a simulation-based approximation of the standard errors.

     Null Pseudo-deviance: 1.469e+09  on 1.059e+09  degrees of freedom
 Residual Pseudo-deviance: 1.051e+03  on 1.059e+09  degrees of freedom
 
AIC: 1059  BIC: 1134  (Smaller is better. MC Std. Err. = 0)

# Adjust the R script for better compatibility and error handling
ro.r('''
library(ergm)
net <- network::network(matrix(edges, byrow=TRUE, ncol=2), directed=TRUE, vertices=node_ids)
for (attr_name in colnames(nodes)) {
    set.vertex.attribute(net, attr_name, nodes[[attr_name]])
}

fit <- ergm(net ~ edges + triangle + mutual + nodecov('Attendance'), estimate = "MPLE")

# Check if fit is valid and prepare summary
if (!is.null(fit)) {
    fit_summary <- summary(fit)  # This should be the raw summary output
    if (!is.null(fit_summary)) {
        fit_df <- data.frame(
            Estimate = fit_summary$coefficients,
            Std.Error = fit_summary$se,
            Z.value = fit_summary$coefficients / fit_summary$se,
            P.value = fit_summary$p.values
        )
        print(fit_df)
        .GlobalEnv$fit_df <- fit_df  # Save DataFrame to global environment
    } else {
        print("Fit summary is NULL or not available.")
    }
} else {
    print("ERGM model did not fit properly.")
}
''')


if 'fit_df' in ro.globalenv and not r['is.null'](ro.globalenv['fit_df']):
            summary_df = pandas2ri.rpy2py(ro.globalenv['fit_df'])
            results = {
                "Estimates": summary_df["Estimate"].tolist(),
                "Std. Errors": summary_df["Std.Error"].tolist(),
                "Z-values": summary_df["Z.value"].tolist(),
                "P-values": summary_df["P.value"].tolist(),
            }
            return JsonResponse(results)
        else:
            logger.error("Model summary is NULL or the DataFrame could not be created.")
            return JsonResponse({"error": "Model did not converge or insufficient data for summary"}, status=500)
    except Exception as e:
        logger.exception("Error during ERGM process: %s", str(e))
        return JsonResponse({"error": str(e)}, status=500)

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