所以我需要在R中使用// delegation implementation details
import kotlin.jvm.JvmClassMappingKt;
import kotlin.jvm.internal.MutablePropertyReference1Impl;
import kotlin.jvm.internal.Reflection;
import kotlin.reflect.KProperty1;
// notNull property delegate from stdlib
import kotlin.properties.Delegates;
import kotlin.properties.ReadWriteProperty;
class JavaClass {
private final ReadWriteProperty<Object, String> x_delegate = Delegates.INSTANCE.notNull();
private final static KProperty1 x_property = Reflection.mutableProperty1(
new MutablePropertyReference1Impl(
JvmClassMappingKt.getKotlinClass(JavaClass.class), "x", "<no_signature>"));
public String getX() {
return x_delegate.getValue(this, x_property);
}
public void setX(String value) {
x_delegate.setValue(this, x_property, value);
}
}
class Usage {
public static void main(String[] args) {
JavaClass instance = new JavaClass();
instance.setX("new value");
System.out.println(instance.getX());
}
}
绘制因子变量的相互作用。我已经能够解决所有问题,除了它的一个重要部分:如何更改被绘制因子的标签。这是一个可复制的示例,显示了该问题:
interplot
一旦我在这里绘制了互动,我就得到了:
现在,我需要能够更改构面中的set.seed(507)
df <- data.frame(
outcome = sample(1:7, 1000, replace = T),
scale = sample(1:7, 1000, replace = T),
dummy = sample(0:2, 1000, replace = T))
# factor the dummy
df$dummyf <- factor(df$dummy)
# linear model
lm.out <- lm(outcome ~ scale * dummyf, data = df)
# interplot
library(interplot)
interplot(lm.out, "dummyf", "scale", plot = T, hist = F, ci = 0.95)
和dummyf1
标签,以便基本上阅读dummyf2
和LABEL1
。这是我尝试的一种可能的解决方案,但这并没有让我得到我需要的东西:
LABEL2
我还试图修改# possible solution?
levels(df$dummyf)[levels(df$dummyf) == 1] <- "LABEL1"
levels(df$dummyf)[levels(df$dummyf) == 2] <- "LABEL2"
# linear model
lm.out.1 <- lm(outcome ~ scale * dummyf, data = df)
# interplot
library(interplot)
interplot(lm.out, "dummyf", "scale", plot = T, hist = F, ci = 0.95)
的方面,因为ggplot2
使用了interplot
,但也无法让它工作。有什么建议?提前谢谢!