sm_predict.RdCalculates kernel-weighted predictions from regression models (i.e. outcomes that can be calculated from the predict function).
sm_predict(data, method, formula, type, newdata = data, method.args = NULL, lambda = 1, kernel = "epanechnikov", dist.method = "euclidean", verbose = FALSE)
| data | data frame |
|---|---|
| method | function to use |
| formula | formula |
| type | type of statistic to smooth (e.g. survival, median survival, etc.) |
| newdata | new data frame. Default is `data`. Only requires covariates from the RHS of `~` and the time component from the outcome for some survival estimators. |
| method.args | List of additional arguments passed on to the modelling function defined by `method` |
| lambda | The radius of the kernel for tri-cubic, Epanechnikov, and flat kernels. The standard deviation for the Gaussian kernel |
| kernel | Specifies the kernel to be used: `epanechnikov`, `tricube`, `gaussian`, and `flat` are accepted. Default is `epanechnikov` |
| dist.method | Specifies the distance measure to be used in the kernel. Default is `euclidean`. Distance measures accepted by |
| verbose | Return full set of results as an attribute. Default is `FALSE` |
sm_predict( mtcars, method = "glm", formula = am ~ mpg, method.args = list(family = binomial(link = "logit")), type = "response" )#>#> # A tibble: 27 x 2 #> am mpg #> <dbl> <dbl> #> 1 1 21 #> 2 1 22.8 #> 3 0 21.4 #> 4 0 18.7 #> 5 0 18.1 #> 6 0 14.3 #> 7 0 24.4 #> 8 0 22.8 #> 9 0 19.2 #> 10 0 17.8 #> 11 0 16.4 #> 12 0 17.3 #> 13 0 15.2 #> 14 0 10.4 #> 15 0 14.7 #> 16 1 32.4 #> 17 1 30.4 #> 18 1 33.9 #> 19 0 21.5 #> 20 0 15.5 #> 21 0 13.3 #> 22 1 27.3 #> 23 1 26 #> 24 1 15.8 #> 25 1 19.7 #> 26 1 15 #> 27 1 21.4#> mpg cyl disp hp drat wt qsec vs am gear carb .fitted #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 4.089578e-01 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 4.089578e-01 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 5.504164e-01 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4.370929e-01 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 3.016828e-01 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 2.728354e-01 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1.461361e-01 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 6.456536e-01 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 5.504164e-01 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 3.238854e-01 #> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 2.580795e-01 #> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1.993910e-01 #> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 2.351012e-01 #> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1.621707e-01 #> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 1.057911e-05 #> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 1.057911e-05 #> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 1.535469e-01 #> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 1.000000e+00 #> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1.000000e+00 #> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1.000000e+00 #> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 4.449265e-01 #> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1.685713e-01 #> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1.621707e-01 #> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1.201609e-01 #> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 3.238854e-01 #> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 9.454960e-01 #> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 7.926169e-01 #> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1.000000e+00 #> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1.774318e-01 #> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 3.448688e-01 #> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1.590070e-01 #> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 4.370929e-01