ahmed_Mobile - 4 months ago 14
R Question

# Producing logistic curve for my logistic regression model

I want to write the code for plotting my logistic regression model, i.e., the "S"-shape logistic curve. How could that be done please as I have have two independent covariates? I'm attaching my data set, and the code for my model. Thank you in advance.

``````239 0.72    1
324.6   0.83    1
331.8   0.95    1
334.3   0.83    1
259.7   0.89    1
212.3   0.88    1
204.7   0.65    1
253.86  0.75    1
258.94  0.85    1
329.66  0.95    0
469.68  1.46    0
459.74  1.11    0
293.2   0.64    0
297.88  0.98    0
267.9   0.82    0
374.1   1.29    0
333.62  0.74    0

dat <- read.table("data.txt")
colnames(dat)<-c("press","v","gender")

# logostic regression
dat\$gender <- factor(dat\$gender)
mylogit<- glm(gender~press+v,data=dat,family="binomial")
summary(mylogit)

######## the code below are irrelevant to making plot, ignore if you want

mylogit\$fitted.values

newdat <- data.frame(t(c(300,0.1)))
colnames(newdat)<-c("press","v")
# this is your new dataset, we name it as "newdat"
pred <- predict(mylogit,newdata = newdat,type="response")
pred # the probability of being in class 1 will stored in this object

pred <- predict(mylogit,newdata = dat,type="response")
pred # the probability of being in class 1 will stored in this object
# accuracy
dat\$pred <- 0
factor(dat\$pred)
dat\$pred[which(pred>0.5)] <- 1

table(dat\$gender,dat\$pred)
``````

Answer

You have 2 continuous, non-categorical variables, so the logistic curve will be a 3D curve. I will offer you two ways for presentation.

• use `persp` function to produce a real 3D smooth curve;
• fix `v` at a number of values, then produce a number of 2D logistic curve (which you called "S"-shape curve).

3D curve

``````press_grid <- seq(200, 480, by = 5)
v_grid <- seq(0.6, 1.5, by = 0.1)
newdat <- data.frame(press = rep(press_grid, times = length(v_grid)), v = rep(v_grid, each = length(press_grid)))
pred <- predict.glm(mylogit, newdata = newdat, type="response")
z <- matrix(pred, length(press_grid))
persp(press_grid, v_grid, z, xlab = "pressure", ylab = "velocity", zlab = "predicted probability", main = "logistic curve (3D)", theta = 30, phi = 20)
``````

You need to first generate a 2D grid. The `newdat` holds this grid, and you can do `plot(newdat)` to see this grid. Then prediction will take place on this grid, by calling `predict.glm(..., type = "response")`. The result `pred` is a vector. To plot it, cast it to a matrix `z`, then invoke `persp` to make 3D plot. `xlab`, `ylab` and `zlab` are labels for three axis. The parameters `theta` and `phi` are used to tweak your viewing angles.

In the above, the marginal grid for `press` and `v` are based on the range of your original data: `range(dat\$press)` and `range(dat\$v)`. We don't make prediction beyond this range too far. But even within this range, you only have 17 observations. So you need still be sceptical on the plot.

Here is the curve:

2D curves

This toy function is useful for making a 2D curve, with `v` fixed as some level:

``````curve_2D_fix_v <- function(model, v = 1, press_grid = seq(200, 480, by = 5), add = FALSE, col = "black") {
newdat <- data.frame(press = press_grid, v = v)
pred <- predict.glm(model, newdat, type = "response")
if (add) lines(press_grid, pred, col = col) else {
plot(press_grid, pred, xlab = "pressure", ylab = "predicted probability", type = "l", col = col, main = "logistic curve (2D)")
abline(h = c(0, 0.5, 1), lty = 2, col = col)
}
}
``````

If `add = FALSE`, it opens a new plotting window; while it is `TRUE`, it plots on previous window (but it is your duty to make sure there is such a window!) The 2D plot gives more information, because you can add a horizontal line at 0, 0.5 and 1.

Let's have a go:

``````curve_2D_fix_v(mylogit, v = 0.4, add = FALSE, col = "black")
curve_2D_fix_v(mylogit, v = 0.6, add = TRUE, col = "red")
curve_2D_fix_v(mylogit, v = 0.8, add = TRUE, col = "green")
curve_2D_fix_v(mylogit, v = 1, add = TRUE, col = "blue")
curve_2D_fix_v(mylogit, v = 1.2, add = TRUE, col = "cyan")
curve_2D_fix_v(mylogit, v = 0.4, add = TRUE, col = "yellow")
``````

Here is the curve:

Discussion

In both plots, we see that the relationship between `gender` (predicted probability) and `v` (velocity) is not very strong. In 2D plot, almost all values of `v` produce the same curve. On the other hand, `press` (pressure) is a strong effect.

Back to your model:

``````> summary(mylogit)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)  8.08326    4.45463   1.815   0.0696 .
press       -0.02575    0.01618  -1.591   0.1115
v           -0.15385    4.83824  -0.032   0.9746
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
``````

You can see that `v` is not significant at all! While strictly speaking, `press` is also not significant at 0.1 level. So this is a very weak model. I suggest you drop variable `v` and do the model again, using `press` as the only variable.

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