On 1 May 2019, at 07:14 , Jens Heumann <jens.heumann at students.unibe.ch> wrote:
Thanks a lot for your hint, David. It finally worked doing:
sapply(unique(data[[st]]), function(s)
+ summary(do.call("lm", list(FO, data, data[[st]] == s,
+ data[[ws]])))$coef[1, ])
[,1] [,2] [,3]
Estimate 1.6269038 -0.1404174 -0.010338774
Std. Error 0.9042738 0.4577001 1.858138516
t value 1.7991275 -0.3067890 -0.005564049
Pr(>|t|) 0.3229600 0.8104951 0.996457853
Best,
Jens
On 30.04.2019 23:03, David Winsemius wrote:
Try using do.call
?
David
Sent from my iPhone
On Apr 30, 2019, at 9:24 AM, Jens Heumann <jens.heumann at students.unibe.ch> wrote:
Hi,
`lm` won't take formula as a parameter when it is within a `sapply`; see example below. Please, could anyone either point me to a syntax error or confirm that this might be a bug?
Best,
Jens
[Disclaimer: This is my first post here, following advice of how to proceed with possible bugs from here: https://www.r-project.org/bugs.html]
SUMMARY
While `lm` alone accepts formula parameter `FO` well, the same within a `sapply` causes an error. When putting everything as parameter but formula `FO`, it's still working, though. All parameters work fine within a similar `for` loop.
MCVE (see data / R-version at bottom)
summary(lm(y ~ x, df1, df1[["z"]] == 1, df1[["w"]]))$coef[1, ]
Estimate Std. Error t value Pr(>|t|)
1.6269038 0.9042738 1.7991275 0.3229600
summary(lm(FO, data, data[[st]] == st1, data[[ws]]))$coef[1, ]
Estimate Std. Error t value Pr(>|t|)
1.6269038 0.9042738 1.7991275 0.3229600
sapply(unique(df1$z), function(s)
+ summary(lm(y ~ x, df1, df1[["z"]] == s, df1[[ws]]))$coef[1, ])
[,1] [,2] [,3]
Estimate 1.6269038 -0.1404174 -0.010338774
Std. Error 0.9042738 0.4577001 1.858138516
t value 1.7991275 -0.3067890 -0.005564049
Pr(>|t|) 0.3229600 0.8104951 0.996457853
sapply(unique(data[[st]]), function(s)
+ summary(lm(FO, data, data[[st]] == s, data[[ws]]))$coef[1, ]) # !!!
Error in eval(substitute(subset), data, env) : object 's' not found
sapply(unique(data[[st]]), function(s)
+ summary(lm(y ~ x, data, data[[st]] == s, data[[ws]]))$coef[1, ])
[,1] [,2] [,3]
Estimate 1.6269038 -0.1404174 -0.010338774
Std. Error 0.9042738 0.4577001 1.858138516
t value 1.7991275 -0.3067890 -0.005564049
Pr(>|t|) 0.3229600 0.8104951 0.996457853
m <- matrix(NA, 4, length(unique(data[[st]])))
for (s in unique(data[[st]])) {
+ m[, s] <- summary(lm(FO, data, data[[st]] == s, data[[ws]]))$coef[1, ]
+ }
[,1] [,2] [,3]
[1,] 1.6269038 -0.1404174 -0.010338774
[2,] 0.9042738 0.4577001 1.858138516
[3,] 1.7991275 -0.3067890 -0.005564049
[4,] 0.3229600 0.8104951 0.996457853
# DATA #################################################################
df1 <- structure(list(x = c(1.37095844714667, -0.564698171396089, 0.363128411337339,
0.63286260496104, 0.404268323140999, -0.106124516091484, 1.51152199743894,
-0.0946590384130976, 2.01842371387704), y = c(1.30824434809425,
0.740171482827397, 2.64977380403845, -0.755998096151299, 0.125479556323628,
-0.239445852485142, 2.14747239550901, -0.37891195982917, -0.638031707027734
), z = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), w = c(0.7, 0.8,
1.2, 0.9, 1.3, 1.2, 0.8, 1, 1)), class = "data.frame", row.names = c(NA,
-9L))
FO <- y ~ x; data <- df1; st <- "z"; ws <- "w"; st1 <- 1
########################################################################