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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,57 +1,51 @@ | ||
| # weightit, multinom | ||
|
|
||
| Code | ||
| print(model_parameters(fit4)) | ||
| print(model_parameters(fit4, exponentiate = TRUE), zap_small = TRUE) | ||
| Output | ||
| # Response level: 2 | ||
| Parameter | Log-Odds | SE | 95% CI | z | p | ||
| ----------------------------------------------------------------- | ||
| (Intercept) | 1.68e-03 | 0.62 | [-1.22, 1.22] | 2.71e-03 | 0.998 | ||
| treat | 0.07 | 0.24 | [-0.39, 0.54] | 0.31 | 0.755 | ||
| age | -0.03 | 0.01 | [-0.05, -0.01] | -2.38 | 0.018 | ||
| educ | -0.02 | 0.05 | [-0.11, 0.08] | -0.33 | 0.738 | ||
| Parameter | Odds Ratio | SE | 95% CI | z | p | ||
| -------------------------------------------------------------- | ||
| (Intercept) | 1.00 | 0.62 | [0.30, 3.39] | 0.00 | 0.998 | ||
| treat | 1.08 | 0.25 | [0.68, 1.71] | 0.31 | 0.755 | ||
| age | 0.97 | 0.01 | [0.95, 0.99] | -2.38 | 0.018 | ||
| educ | 0.98 | 0.05 | [0.89, 1.08] | -0.33 | 0.738 | ||
| # Response level: 3 | ||
| Parameter | Log-Odds | SE | 95% CI | z | p | ||
| ---------------------------------------------------------------- | ||
| (Intercept) | -3.01 | 0.71 | [-4.40, -1.61] | -4.23 | < .001 | ||
| treat | 0.16 | 0.25 | [-0.32, 0.64] | 0.67 | 0.502 | ||
| age | -1.70e-04 | 0.01 | [-0.02, 0.02] | -0.01 | 0.989 | ||
| educ | 0.18 | 0.05 | [ 0.08, 0.29] | 3.51 | < .001 | ||
| Parameter | Odds Ratio | SE | 95% CI | z | p | ||
| --------------------------------------------------------------- | ||
| (Intercept) | 0.05 | 0.04 | [0.01, 0.20] | -4.23 | < .001 | ||
| treat | 1.18 | 0.29 | [0.73, 1.91] | 0.67 | 0.502 | ||
| age | 1.00 | 0.01 | [0.98, 1.02] | -0.01 | 0.989 | ||
| educ | 1.20 | 0.06 | [1.08, 1.33] | 3.51 | < .001 | ||
| Message | ||
| Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed | ||
| using a Wald z-distribution approximation. | ||
| The model has a log- or logit-link. Consider using `exponentiate = | ||
| TRUE` to interpret coefficients as ratios. | ||
|
|
||
| # weightit, ordinal | ||
|
|
||
| Code | ||
| print(model_parameters(fit5)) | ||
| print(model_parameters(fit5, exponentiate = TRUE), zap_small = TRUE) | ||
| Output | ||
| # Fixed Effects | ||
| Parameter | Log-Odds | SE | 95% CI | z | p | ||
| ---------------------------------------------------------------- | ||
| treat | 0.11 | 0.19 | [-0.25, 0.48] | 0.60 | 0.549 | ||
| age | -7.77e-03 | 9.97e-03 | [-0.03, 0.01] | -0.78 | 0.436 | ||
| educ | 0.11 | 0.04 | [ 0.03, 0.18] | 2.70 | 0.007 | ||
| Parameter | Odds Ratio | SE | 95% CI | z | p | ||
| ------------------------------------------------------------ | ||
| treat | 1.12 | 0.21 | [0.78, 1.61] | 0.60 | 0.549 | ||
| age | 0.99 | 0.01 | [0.97, 1.01] | -0.78 | 0.436 | ||
| educ | 1.11 | 0.04 | [1.03, 1.20] | 2.70 | 0.007 | ||
| # Intercept | ||
| Parameter | Log-Odds | SE | 95% CI | z | p | ||
| ---------------------------------------------------------- | ||
| 1|2 | 1.19 | 0.52 | [0.17, 2.20] | 2.30 | 0.022 | ||
| 2|3 | 2.29 | 0.51 | [1.28, 3.29] | 4.47 | < .001 | ||
| Parameter | Odds Ratio | SE | 95% CI | z | p | ||
| ------------------------------------------------------------- | ||
| 1|2 | 3.28 | 1.70 | [1.19, 9.04] | 2.30 | 0.022 | ||
| 2|3 | 9.84 | 5.03 | [3.61, 26.81] | 4.47 | < .001 | ||
| Message | ||
| Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed | ||
| using a Wald z-distribution approximation. | ||
| The model has a log- or logit-link. Consider using `exponentiate = | ||
| TRUE` to interpret coefficients as ratios. | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,50 +1,43 @@ | ||
| skip_if_not_installed("WeightIt") | ||
| skip_if_not_installed("cobalt") | ||
| skip_if_not_installed("insight", minimum_version = "0.20.4") | ||
| skip_if_not_installed("withr") | ||
|
|
||
| withr::local_options( | ||
| list(parameters_warning_exponentiate = TRUE), | ||
| test_that("weightit, multinom", { | ||
| data("lalonde", package = "cobalt") | ||
| set.seed(1234) | ||
| # Logistic regression ATT weights | ||
| w.out <- WeightIt::weightit( | ||
| treat ~ age + educ + married + re74, | ||
| data = lalonde, | ||
| method = "glm", | ||
| estimand = "ATT" | ||
| ) | ||
| lalonde$re78_3 <- factor(findInterval(lalonde$re78, c(0, 5e3, 1e4))) | ||
| test_that("weightit, multinom", { | ||
| data("lalonde", package = "cobalt") | ||
| set.seed(1234) | ||
| # Logistic regression ATT weights | ||
| w.out <- WeightIt::weightit( | ||
| treat ~ age + educ + married + re74, | ||
| data = lalonde, | ||
| method = "glm", | ||
| estimand = "ATT" | ||
| ) | ||
| lalonde$re78_3 <- factor(findInterval(lalonde$re78, c(0, 5e3, 1e4))) | ||
|
|
||
| fit4 <- WeightIt::multinom_weightit( | ||
| re78_3 ~ treat + age + educ, | ||
| data = lalonde, | ||
| weightit = w.out | ||
| ) | ||
| expect_snapshot(print(model_parameters(fit4))) | ||
| }) | ||
| ) | ||
| fit4 <- WeightIt::multinom_weightit( | ||
| re78_3 ~ treat + age + educ, | ||
| data = lalonde, | ||
| weightit = w.out | ||
| ) | ||
| expect_snapshot(print(model_parameters(fit4, exponentiate = TRUE), zap_small = TRUE)) | ||
| }) | ||
|
|
||
| withr::local_options( | ||
| list(parameters_warning_exponentiate = TRUE), | ||
| test_that("weightit, ordinal", { | ||
| data("lalonde", package = "cobalt") | ||
| set.seed(1234) | ||
| # Logistic regression ATT weights | ||
| w.out <- WeightIt::weightit( | ||
| treat ~ age + educ + married + re74, | ||
| data = lalonde, | ||
| method = "glm", | ||
| estimand = "ATT" | ||
| ) | ||
| lalonde$re78_3 <- factor(findInterval(lalonde$re78, c(0, 5e3, 1e4))) | ||
| test_that("weightit, ordinal", { | ||
| data("lalonde", package = "cobalt") | ||
| set.seed(1234) | ||
| # Logistic regression ATT weights | ||
| w.out <- WeightIt::weightit( | ||
| treat ~ age + educ + married + re74, | ||
| data = lalonde, | ||
| method = "glm", | ||
| estimand = "ATT" | ||
| ) | ||
| lalonde$re78_3 <- factor(findInterval(lalonde$re78, c(0, 5e3, 1e4))) | ||
|
|
||
| fit5 <- WeightIt::ordinal_weightit( | ||
| ordered(re78_3) ~ treat + age + educ, | ||
| data = lalonde, | ||
| weightit = w.out | ||
| ) | ||
| expect_snapshot(print(model_parameters(fit5))) | ||
| }) | ||
| ) | ||
| fit5 <- WeightIt::ordinal_weightit( | ||
| ordered(re78_3) ~ treat + age + educ, | ||
| data = lalonde, | ||
| weightit = w.out | ||
| ) | ||
| expect_snapshot(print(model_parameters(fit5, exponentiate = TRUE), zap_small = TRUE)) | ||
| }) |