I was trying to keep only "Yes" in the out put and keep "No" in the back ground instead of all levels are printed out.
i tried the below code
table4<-Age_group_socio %>%
tbl_summary(by=Age england,missing="no",value =list(c(Cardiac diseases~'Yes', Hypertension~'Yes',Liver diseases~'Yes', Renal diseases~'Yes', Diabetes~'Yes', Neurological diseases~"Yes", Malignancy~'Yes', Malaria~'Yes', HIV~'Yes', Other immune deficiency diseases~'Yes',Tuberculosis~'Yes',Other chronic lung diseases~'Yes',Measles~"Yes" ))) %>%
bold_labels()
Error: 'value' argument must be a list of formulas or named list (see ?syntax). LHS of the formula is the variable specification, and the RHS is the value specification: list(stage ~ "T1")
Related
I'm trying to implement the 'Sport Scheduling Problem' (with a Round-Robin approach to break symmetries). The actual problem is of no importance. I simply want to declare the value at x[1,1] to be the set {1,2} and base the sets in the same column upon the first set. This is modelled as in the code below. The output is included in a screenshot below it. The problem is that the first set is not printed as a set but rather some sort of range while the values at x[2,1] and x[3,1] are indeed printed as sets and x[4,1] again as a range. Why is this? I assume that in the declaration of x that set of 1..n is treated as an integer but if it is not, how to declare it as integers?
EDIT: ONLY the first column of the output is of importance.
int: n = 8;
int: nw = n-1;
int: np = n div 2;
array[1..np, 1..nw] of var set of 1..n: x;
% BEGIN FIX FIRST WEEK $
constraint(
x[1,1] = {1, 2}
);
constraint(
forall(t in 2..np) (x[t,1] = {t+1, n+2-t} )
);
solve satisfy;
output[
"\(x[p,w])" ++ if w == nw then "\n" else "\t" endif | p in 1..np, w in 1..nw
]
Backend solver: Gecode
(Here's a summarize of my comments above.)
The range syntax is simply a shorthand for contiguous values in a set: 1..8 is a shorthand of the set {1,2,3,4,5,6,7,8}, and 5..6 is a shorthand for the set {5,6}.
The reason for this shorthand is probably since it's often - and arguably - easier to read the shorthand version than the full list, especially if it's a long list of integers, e.g. 1..1024. It also save space in the output of solutions.
For the two set versions, e.g. {1,2}, this explicit enumeration might be clearer to read than 1..2, though I tend to prefer the shorthand version in all cases.
I am upgrading my Matlab from 2013b to 2018b and have found out that MathWorks have made quite a few changes to the GUI's.
One problem I am having is getting the value of checkbox. The line below is the code I used to use but now it doesn't work.
if get(handles.check_perf_attr,'Value') == 1
The error message is,
Undefined operator '==' for input arguments of type 'cell'.
So I tried the line below to just get the value that is being returned and then apply some logic.
tValue = get(handles.check_perf_attr,'Value');
However tValue is 2 x 1 cell which in (1, 1) = 0 & (2, 1) = 1. I don't really understand this as surely a checkbox can only be one value true (1) or false (0)?
get returns a cell array with values when applied to an array of handles.
Thus, I think your problem is that handles.check_perf_attr contains two handles, not one.
"Dot notation is a new syntax to access object properties starting in R2014b."
so try
if handles.check_perf_attr.Value == 1
or
tValue = handles.check_perf_attr.Value;
I have some simple time to event data, no covariates. I was trying to fit a Weibull distribution to it. So I have the following code. Everything looks good until I load my initials. It says "this chain contains uninitialized variables". But I don't understand. I think Weibull dist only has 2 parameters, and I already specified them all. Could you please advise? Thanks!
model
{
for(i in 1 : N) {
t[i] ~ dweib(r, mu)I(t.cen[i],)
}
mu ~ dexp(0.001)
r ~ dexp(0.001)
}
# Data
list(
t.cen=c(0,3.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.95,23.98,33.08),
t=c(2.34,NA,5.16,5.63,6.17,6.8,7.03,8.05,8.13,8.36,8.83,10.16,
10.55,10.94,11.48,11.95,13.05,13.59,16.02,20.08,NA,NA,
NA),
N=23
)
# Initial values
list(
r=3,mu=3
)
The other uninitialised variables are the missing (NA) values in the vector of t. Remember that the BUGS language makes no distinction between data and parameters, and that supplying something as data with the value NA is equivalent to not supplying it as data.
I'm new to the SPSS macro syntax and had a hard time trying to label variables based on a simple loop counter. Here's what I tried to do:
define !make_indicatorvars()
!do !i = 1 !to 10.
!let !indicvar = !concat('indexvar_value_', !i, '_ind')
compute !indicvar = 0.
if(indexvar = !i) !indicvar = 1.
variable labels !indicvar 'Indexvar has value ' + !quote(!i).
value labels !indicvar 0 "No" 1 "Yes".
!doend
!enddefine.
However, when I run this, I get the following warnings:
Warning # 207 on line ... in column ... Text: ...
A '+' was found following a text string, indicating continuation, but the next non-blank character was not a quotation mark or an apostrophe.
Warning # 4465 in column ... Text: ...
An invalid symbol appears on the VAR LABELS command where a slash was
expected. All text through the next slash will be be ignored.
Indeed the label is then only 'Indexvar has value '.
Upon using "set mprint on printback on", the following code was printed:
variable labels indexvar_value_1_ind 'Indexvar has value ' '1'
So it appears that SPSS seems to somehow remove the "+" which is supposed to concatenate the two strings, but why?
The rest of the macro worked fine, it's only the variable labels command that's causing problems.
Try:
variable labels !indicvar !quote(!concat('Indexvar has value ',!i)).
Also note:
compute !indicvar = 0.
if(indexvar = !i) !indicvar = 1.
Can be simplified as:
compute !indicvar = (indexvar = !i).
Where the right hand side of the COMPUTE equation evaluates to equal TRUE a 1 (one) is assigned else if FALSE a 0 (zero) is assigned. Using just a single compute in this way not only reduce the lines of code, it will also make the transformations more efficient/quicker to run.
You might consider the SPSSINC CREATE DUMMIES extension command. It will automatically construct a set of dummies for a variable and label them with the values or value labels. It also creates a macro that lists all the variables. There is no need to enumerate the values. It creates dummies for all the values in the data.
It appears on the Transform menu as long as the Python Essentials are installed. Here is an example using the employee data.sav file shipped with Statistics.
SPSSINC CREATE DUMMIES VARIABLE=jobcat
ROOTNAME1=job
/OPTIONS ORDER=A USEVALUELABELS=YES USEML=YES OMITFIRST=NO
MACRONAME1="!jobcat".
I've finally decided to put the sort.data.frame method that's floating around the internet into an R package. It just gets requested too much to be left to an ad hoc method of distribution.
However, it's written with arguments that make it incompatible with the generic sort function:
sort(x,decreasing,...)
sort.data.frame(form,dat)
If I change sort.data.frame to take decreasing as an argument as in sort.data.frame(form,decreasing,dat) and discard decreasing, then it loses its simplicity because you'll always have to specify dat= and can't really use positional arguments. If I add it to the end as in sort.data.frame(form,dat,decreasing), then the order doesn't match with the generic function. If I hope that decreasing gets caught up in the dots `sort.data.frame(form,dat,...), then when using position-based matching I believe the generic function will assign the second position to decreasing and it will get discarded. What's the best way to harmonize these two functions?
The full function is:
# Sort a data frame
sort.data.frame <- function(form,dat){
# Author: Kevin Wright
# http://tolstoy.newcastle.edu.au/R/help/04/09/4300.html
# Some ideas from Andy Liaw
# http://tolstoy.newcastle.edu.au/R/help/04/07/1076.html
# Use + for ascending, - for decending.
# Sorting is left to right in the formula
# Useage is either of the following:
# sort.data.frame(~Block-Variety,Oats)
# sort.data.frame(Oats,~-Variety+Block)
# If dat is the formula, then switch form and dat
if(inherits(dat,"formula")){
f=dat
dat=form
form=f
}
if(form[[1]] != "~") {
stop("Formula must be one-sided.")
}
# Make the formula into character and remove spaces
formc <- as.character(form[2])
formc <- gsub(" ","",formc)
# If the first character is not + or -, add +
if(!is.element(substring(formc,1,1),c("+","-"))) {
formc <- paste("+",formc,sep="")
}
# Extract the variables from the formula
vars <- unlist(strsplit(formc, "[\\+\\-]"))
vars <- vars[vars!=""] # Remove spurious "" terms
# Build a list of arguments to pass to "order" function
calllist <- list()
pos=1 # Position of + or -
for(i in 1:length(vars)){
varsign <- substring(formc,pos,pos)
pos <- pos+1+nchar(vars[i])
if(is.factor(dat[,vars[i]])){
if(varsign=="-")
calllist[[i]] <- -rank(dat[,vars[i]])
else
calllist[[i]] <- rank(dat[,vars[i]])
}
else {
if(varsign=="-")
calllist[[i]] <- -dat[,vars[i]]
else
calllist[[i]] <- dat[,vars[i]]
}
}
dat[do.call("order",calllist),]
}
Example:
library(datasets)
sort.data.frame(~len+dose,ToothGrowth)
Use the arrange function in plyr. It allows you to individually pick which variables should be in ascending and descending order:
arrange(ToothGrowth, len, dose)
arrange(ToothGrowth, desc(len), dose)
arrange(ToothGrowth, len, desc(dose))
arrange(ToothGrowth, desc(len), desc(dose))
It also has an elegant implementation:
arrange <- function (df, ...) {
ord <- eval(substitute(order(...)), df, parent.frame())
unrowname(df[ord, ])
}
And desc is just an ordinary function:
desc <- function (x) -xtfrm(x)
Reading the help for xtfrm is highly recommended if you're writing this sort of function.
There are a few problems there. sort.data.frame needs to have the same arguments as the generic, so at a minimum it needs to be
sort.data.frame(x, decreasing = FALSE, ...) {
....
}
To have dispatch work, the first argument needs to be the object dispatched on. So I would start with:
sort.data.frame(x, decreasing = FALSE, formula = ~ ., ...) {
....
}
where x is your dat, formula is your form, and we provide a default for formula to include everything. (I haven't studied your code in detail to see exactly what form represents.)
Of course, you don't need to specify decreasing in the call, so:
sort(ToothGrowth, formula = ~ len + dose)
would be how to call the function using the above specifications.
Otherwise, if you don't want sort.data.frame to be an S3 generic, call it something else and then you are free to have whatever arguments you want.
I agree with #Gavin that x must come first. I'd put the decreasing parameter after the formula though - since it probably isn't used that much, and hardly ever as a positional argument.
The formula argument would be used much more and therefore should be the second argument. I also strongly agree with #Gavin that it should be called formula, and not form.
sort.data.frame(x, formula = ~ ., decreasing = FALSE, ...) {
...
}
You might want to extend the decreasing argument to allow a logical vector where each TRUE/FALSE value corresponds to one column in the formula:
d <- data.frame(A=1:10, B=10:1)
sort(d, ~ A+B, decreasing=c(A=TRUE, B=FALSE)) # sort by decreasing A, increasing B