Fix typo

README.md

@@ -123,6 +123,21 @@ follows.
 
 ``` r
 summary(fit, test = TRUE)
+#> 
+#> Call:
+#> ulsif(df_numerator = numerator_data$x5, df_denominator = denominator_data$x5,     nsigma = 5, nlambda = 5)
+#> 
+#> Kernel Information:
+#>   Kernel type: Gaussian with L2 norm distances
+#>   Number of kernels: 200
+#> 
+#> Optimal sigma: 0.8951539
+#> Optimal lambda: 0.03162278
+#> Optimal kernel weights (loocv): num [1:201] 0.14182 0.01957 0.00488 0.01596 0.01461 ...
+#>  
+#> Pearson divergence between P(nu) and P(de): 0.2871
+#> Pr(P(nu)=P(de)) < .001
+#> Bonferroni-corrected for testing with r(x) = P(nu)/P(de) AND r*(x) = P(de)/P(nu).
 ```
 
 The probability that numerator and denominator samples share a common
@@ -153,7 +168,8 @@ ggplot() +
         text = element_text(size = 20))
 ```
 
-<img src="man/figures/README-plot-univ-1.svg" data-fig-align="center" />
+<img src="man/figures/README-plot-univ-1.svg" style="width:15cm"
+data-fig-align="center" />
 
 ### Categorical data
 
@@ -185,9 +201,9 @@ aggregate(
   FUN = unique
 )
 #>   numerator_data$x1 predict(fit_cat)
-#> 1                 A        1.3731403
-#> 2                 B        1.3621401
-#> 3                 C        0.6291639
+#> 1                 A        1.3596470
+#> 2                 B        1.3687135
+#> 3                 C        0.6290922
 
 
 table(numerator_data$x1) / table(denominator_data$x1)
@@ -209,6 +225,21 @@ fit_all <- ulsif(
 )
 
 summary(fit_all, test = TRUE, parallel = TRUE)
+#> 
+#> Call:
+#> ulsif(df_numerator = data.frame(lapply(numerator_data, as.numeric)),     df_denominator = data.frame(lapply(denominator_data, as.numeric)))
+#> 
+#> Kernel Information:
+#>   Kernel type: Gaussian with L2 norm distances
+#>   Number of kernels: 200
+#> 
+#> Optimal sigma: 1.473857
+#> Optimal lambda: 0.3359818
+#> Optimal kernel weights (loocv): num [1:201] 0.1771 -0.0255 -0.0131 0.0979 0.0354 ...
+#>  
+#> Pearson divergence between P(nu) and P(de): 0.4736
+#> Pr(P(nu)=P(de)) < .001
+#> Bonferroni-corrected for testing with r(x) = P(nu)/P(de) AND r*(x) = P(de)/P(nu).
 ```
 
 ### Other density ratio estimation functions
@@ -251,7 +282,7 @@ ggplot(data = NULL, aes(x = newx5)) +
         text = element_text(size = 20))
 ```
 
-<img src="man/figures/README-plot-methods-1.svg"
+<img src="man/figures/README-plot-methods-1.svg" style="width:15cm"
 data-fig-align="center" />
 
 The figure directly shows that `ulsif()` and `kliep()` come rather close

README.qmd

@@ -85,7 +85,7 @@ without estimating the numerator and denominator density separately. Density rat
 #| out-width: 15cm
 #| fig-dpi: 500
 #| fig-format: svg
-#| fig-align: "center"
+#| fig-align: center
 
 set.seed(1)
 library(ggplot2)
@@ -102,9 +102,7 @@ dratio <- function(x, p, dif, mu, sd) {
 fit  <- densityratio::ulsif(
   df_numerator = densityratio::numerator_data$x5, 
   df_denominator = densityratio::denominator_data$x5,
-  centers = c(densityratio::numerator_data$x5, densityratio::denominator_data$x5),
-  nsigma = 2,
-  nlambda = 2
+  centers = c(densityratio::numerator_data$x5, densityratio::denominator_data$x5)
 )
 
 ggplot() +
@@ -198,7 +196,6 @@ summary(fit)
 To formally evaluate whether the numerator and denominator densities differ significantly, you can perform a two-sample homogeneity test as follows.
 
 ```{r}
-#| eval: false
 summary(fit, test = TRUE)
 ```
 
@@ -210,8 +207,9 @@ Using these variables, we can obtain the estimated density ratio using `predict(
 
 ```{r}
 #| label: plot-univ
-#| fig-height: 7
+#| fig-height: 6
 #| fig-width: 10
+#| out-width: 15cm
 #| fig-dpi: 500
 #| fig-format: svg
 #| fig-align: "center"
@@ -267,7 +265,6 @@ table(numerator_data$x1) / table(denominator_data$x1)
 After transforming all variables to numeric variables, it is possible to calculate the density ratio over the entire multivariate space of the data.
 
 ```{r}
-#| eval: false
 fit_all <- ulsif(
   df_numerator = numerator_data |> lapply(as.numeric) |> data.frame(),
   df_denominator = denominator_data |> lapply(as.numeric) |> data.frame()
@@ -287,8 +284,9 @@ Besides `ulsif()`, the package contains several other functions to estimate a de
 
 ```{r}
 #| label: plot-methods
-#| fig-height: 7
+#| fig-height: 6
 #| fig-width: 10
+#| out-width: 15cm
 #| fig-dpi: 500
 #| fig-format: svg
 #| fig-align: "center"