Is there a way to extract the mean and p-value from a t.test output and create a table that includes the features, mean, and p-value? Say there are 10 columns put through t.test, and that means there are 10 means, and 10 p-values. How would I be able to create a table which only shows those specific items?
here is an example: data (iris):
a. b. c. d. e.
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
t.test(a)
t.test(b) #...ect we obtain the mean and p-value.
this is the output im looking for:
feature mean p-val
col1 0.01 0.95
col2 0.01 0.95
.
.
.
coln
hope it makes sense!
Using the iris built in data set as an example
t(sapply(iris[, 1:4], function(i){
t.test(i)[c(5,3)]
}))
The sapply() function is iteratively performing that custom function - which performs a t-test on a variable and returns the estimate and p-value - through columns 1 to 4 of iris. That is then transposed by t() to rotate the data to the desired shape. You can store that as a data.frame using data.frame() and use row.names() to get the variable names into a new column on that if you like.
values <- t(sapply(iris[, 1:4], function(i){
t.test(i)[c(5,3)]
}))
values <- data.frame("feature"=row.names(values), values)
row.names(values) <- NULL
values
Beware multiple testing though...
I'm not sure what's going on here, but when I try to run ggplots, it tells me that u and u1 are not valid lists. Did I enter u and u1 incorrectly, that it thinks these are functions, did I forget something, or did I enter things wrong into ggplots?
u1 <- function(x,y){max(utilityf1(x))}
utilityc1 <- data.frame("utilityc1" =
u(c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,20),
c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,20)))
utilityc1 <- data.frame("utilityc1" =
u1(c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,20),
c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,20)))
hhcomp <- data.frame(
pqx, pqy, utility, hours, p1qx, p1qy, utilit, utilityc1,
utilityc, u,u1, o, o1, o2
)
library(ggplot2)
ggplot(hhcomp, aes(x=utility, y=consumption))+
coord_cartesian(xlim = c(0, 16) )+
ylim(0,20)+
labs(x = "leisure(hours)",y="counsumption(units)")+
geom_line(aes(x = u, y = consumption))+
geom_line(aes(x = u1, y = consumption))
I'm not sure what else to explain, so if someone could provide some help on providing code to stack overflow that would be useful. I'm also not sure how much of a description to have, I should have enough code to be reproducible, but there is a problem that Stack Overflow only allows so much code, so it would be good to know the right amount to add.
I think you may need to read the documentation for ggplot2 and maybe r in general.
data.frame
For starters, a data.frame object is a collection of vectors appended together column wise. Most of what you have defined as inputs for hhcomp are functions, which cannot be stored as a data.frame. A canonical example of a data frame in r is iris
head(iris)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 5.1 3.5 1.4 0.2 setosa
#2 4.9 3.0 1.4 0.2 setosa
#3 4.7 3.2 1.3 0.2 setosa
#4 4.6 3.1 1.5 0.2 setosa
#5 5.0 3.6 1.4 0.2 setosa
#6 5.4 3.9 1.7 0.4 setosa
str(iris) #print the structure of an r object
#'data.frame': 150 obs. of 5 variables:
# $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
# $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
# $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
# $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
functions
There is a lot going on with your functions. Nested functions are fine, but it seems as though you are failing to pass all values on. This probably means you are trying to apply R's scoping rules but this makes code ambiguous of where values are found.
With the currently defined functions, calling u(1:2,3:4) passes 1:2 to utilityf but utilityf's y argument is never assigned (but with r's lazy evaluation we reach a different error before r realizes that this value is missing). The next function that gets evaluated in this nest is p1qyf which is defined as follows
p1qyf <- function(y){(w1*16)-(w1*x)}
with this definition, it does not matter what you pass to the argument y it will never be used and will always return the same thing.
#with only the function defined
p1qyf()
#Error in p1qyf() : object 'w1' not found
#defining w1
w1 <- 1.5
p1qyf()
#Error in p1qyf() : object 'x' not found
x <- 10:20
#All variables defined in the function
#can now be found in the global environment
#thus the function can be called with no errors because
#w1 and x are defined somewhere...
p1qyf() #nothing assigned to y
[1] 9.0 7.5 6.0 4.5 3.0 1.5 0.0 -1.5 -3.0 -4.5 -6.0
p1qyf(y = iris) #a data.frame assigned to y
[1] 9.0 7.5 6.0 4.5 3.0 1.5 0.0 -1.5 -3.0 -4.5 -6.0
p1qyf(y = foo_bar) #an object that hasn't even been assigned yet
[1] 9.0 7.5 6.0 4.5 3.0 1.5 0.0 -1.5 -3.0 -4.5 -6.0
I imagine you actually intend to define it this way
p1qyf <- function(y){(w1*16)-(w1*y)}
#Now what we pass to it affects the output
p1qyf(1:10)
#[1] 22.5 21.0 19.5 18.0 16.5 15.0 13.5 12.0 10.5 9.0
head(p1qyf(iris))
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 16.35 18.75 21.90 23.7 NA
#2 16.65 19.50 21.90 23.7 NA
#3 16.95 19.20 22.05 23.7 NA
#4 17.10 19.35 21.75 23.7 NA
#5 16.50 18.60 21.90 23.7 NA
#6 15.90 18.15 21.45 23.4 NA
You can improve this further by defining more arguments so that R doesn't need to search for missing values with it's scoping rules
p1qyf <- function(y, w1 = 1.5){(w1*16)-(w1*y)}
#w1 is defaulted to 1.5 and doesn't need to be searched for.
I would spend some time looking into your functions because they are unclear and some, such as your p1qyf, do not fully use the arguments they are passed.
ggplot
ggplot takes some type of structured data object such as data.frame tbl_df, and allows plotting. The aes mappings can take the symbol names of the column headers you wish to map. Continuing with iris as an example.
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species))+
geom_point() +
geom_line()
I hope this helps clears up why you may be getting some errors. Honestly though, if you were actually able to declare a data.frame then the problem here is that your post is still not that reproducible. Good luck
pqxf <- function(x){(1)*(y)} # replace 1 with py and assign a value to py
pqyf <- function(y){(w * 16)-(w * x)} #
utilityf <- function(x, y) { (pqyf(x)) * ((pqxf(y)))} # the utility function C,l
hours <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,20)
w1 <- 1.5
p1qxf <- function(x){(1)*(y)} # replace 1 with py and assign a value to p1y
p1qyf <- function(y){(w1 * 16)-(w1 * x)} #
utilityf1 <- function(x, y) { (p1qyf(x)) * ((p1qxf(y)))} # the utility function (C,l)
utilitycf <- function(x,y){max(utilityf(x))/((pqyf(y)))}
utilityc1f <- function(x,y){max(utilityf1(x))/((pqyf(y)))}
u <- function(x,y){max(utilityf(x))}
u1 <- function(x,y){max(utilityf1(x))}```
I estimate a randomForest, then run the randomForest.predict function on some hold-out data.
What I would like to do is (preferably) append the prediction for each row to the dataframe containing the holdout data as a new column, or (second choice) save the (row number in test data, prediction for that row) as a .csv file.
What I can't do is access the internals of the results object in a way that lets me do that. I'm new to R so I appreciate your help.
I have:
res <-predict(forest_tst1,
test_d,
type="response")
which successfully gives me a bunch of predictions.
The following is not valid R, but ideally I would do something like:
test_d$predicted_value <- results[some_field_of_the_results]
or,
for i = 1:nrow(test_d)
test_d[i, new_column] = results[prediction_for_row_i]
end
Basically I just want a column of predicted 1's or 0's corresponding to rows in test_d. I've been trying to use the following commands to get at the internals of the res object, but I've not found anything that's helped me.
attributes(res)
names(res)
Finally - I'm a bit confused by the following if anyone can explain!
typeof(res) = "integer"
Edit: I can do
res != test_d$gold_label
which is if anything a little confusing, because I'm comparing a column and a non-column object (??), and
length(res) = 2053
and res appears to be indexable
attributes(res[1])
$names
[1] "6836"
$levels
[1] "0" "1"
$class
[1] "factor"
but I can't select out the sub-parts in a sensible way
> res[1][1]
6836
0
Levels: 0 1
> res[1]["levels"]
<NA>
<NA>
Levels: 0 1
If understand right, all you are trying to do is add predictions to your Test Data?
ind <- sample(2, nrow(iris), replace = TRUE, prob=c(0.8, 0.2))
TestData = iris[ind == 2,] ## Generate Test Data
iris.rf <- randomForest(Species ~ ., data=iris[ind == 1,]) ## Build Model
iris.pred <- predict(iris.rf, iris[ind == 2,]) ## Get Predictions
TestData$Predictions <- iris.pred ## Append the Predictions Column
OutPut:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Predictions
9 4.4 2.9 1.4 0.2 setosa setosa
16 5.7 4.4 1.5 0.4 setosa setosa
17 5.4 3.9 1.3 0.4 setosa setosa
32 5.4 3.4 1.5 0.4 setosa setosa
42 4.5 2.3 1.3 0.3 setosa setosa
46 4.8 3.0 1.4 0.3 setosa setosa
I am following an example of svd, but I still don't know how to reduce the dimension of the final matrix:
a <- round(runif(10)*100)
dat <- as.matrix(iris[a,-5])
rownames(dat) <- c(1:10)
s <- svd(dat)
pc.use <- 1
recon <- s$u[,pc.use] %*% diag(s$d[pc.use], length(pc.use), length(pc.use)) %*% t(s$v[,pc.use])
But recon still have the same dimension. I need to use this for Semantic analysis.
The code you provided does not reduce the dimensionality. Instead it takes first principal component from your data, removes the rest of principal components, and then reconstructs the data with only one PC.
You can check that this is happening by inspecting the rank of the final matrix:
library(Matrix)
rankMatrix(dat)
as.numeric(rankMatrix(dat))
[1] 4
as.numeric(rankMatrix(recon))
[1] 1
If you want to reduce dimensionality (number of rows) - you can select some principal principal components and compute the scores of your data on those components instead.
But first let's make some things clear about your data - it seems you have 10 samples (rows) with 4 features (columns). Dimensionality reduction will reduce the 4 features to a smaller set of features.
So you can start by transposing your matrix for svd():
dat <- t(dat)
dat
1 2 3 4 5 6 7 8 9 10
Sepal.Length 6.7 6.1 5.8 5.1 6.1 5.1 4.8 5.2 6.1 5.7
Sepal.Width 3.1 2.8 4.0 3.8 3.0 3.7 3.0 4.1 2.8 3.8
Petal.Length 4.4 4.0 1.2 1.5 4.6 1.5 1.4 1.5 4.7 1.7
Petal.Width 1.4 1.3 0.2 0.3 1.4 0.4 0.1 0.1 1.2 0.3
Now you can repeat the svd. Centering the data before this procedure is advisable:
s <- svd(dat - rowMeans(dat))
Principal components can be obtained by projecting your data onto PCs.
PCs <- t(s$u) %*% dat
Now if you want to reduce dimensionality by eliminating PCs with low variance you can do so like this:
dat2 <- PCs[1:2,] # would select first two PCs.
After running a knn classification in (R)[http://www.r-project.org/] is there a way to list the predictions that were made for each of the test cases?
I know how to get the confusion matrix, but I'd also like the detailed results of the test phase as opposed to just the summary.
Would I have to run each case back through the model, as if doing post model development predictions? Or is the information I need an output of the test phase?
I'm confused. That seems to be exactly what knn returns. Adapting the example from the help page for ?knn
library(class)
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
fit <- knn(train, test, cl, k = 3, prob=TRUE)
If i combine the results with the test data, i get
head(data.frame(test, pred=fit, prob=attr(fit, "prob")))
# Sepal.L. Sepal.W. Petal.L. Petal.W. pred prob
# 1 5.0 3.0 1.6 0.2 s 1
# 2 5.0 3.4 1.6 0.4 s 1
# 3 5.2 3.5 1.5 0.2 s 1
# 4 5.2 3.4 1.4 0.2 s 1
# 5 4.7 3.2 1.6 0.2 s 1
# 6 4.8 3.1 1.6 0.2 s 1
so there's a prediction for each test row.